WO2020155907A1 - Procédé et appareil pour la génération d'un modèle de conversion au style cartoon - Google Patents

Procédé et appareil pour la génération d'un modèle de conversion au style cartoon Download PDF

Info

Publication number
WO2020155907A1
WO2020155907A1 PCT/CN2019/126379 CN2019126379W WO2020155907A1 WO 2020155907 A1 WO2020155907 A1 WO 2020155907A1 CN 2019126379 W CN2019126379 W CN 2019126379W WO 2020155907 A1 WO2020155907 A1 WO 2020155907A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
network
sample
comic style
comic
Prior art date
Application number
PCT/CN2019/126379
Other languages
English (en)
Chinese (zh)
Inventor
李华夏
Original Assignee
北京字节跳动网络技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 北京字节跳动网络技术有限公司 filed Critical 北京字节跳动网络技术有限公司
Publication of WO2020155907A1 publication Critical patent/WO2020155907A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image

Definitions

  • the embodiments of the present disclosure relate to the field of computer technology, and in particular to a method and apparatus for generating a comic style conversion model.
  • some image processing software can process images and convert them into images of other styles. For example, change the color and shape of certain areas in the image.
  • These software usually include models for converting images into other styles of images.
  • the training process of the model usually involves inputting an image, comparing the image with its corresponding images of other styles, and optimizing the parameters of the model according to the difference between the two images.
  • the embodiments of the present disclosure propose a method and apparatus for generating a comic style conversion model, and a method and apparatus for generating a comic style image.
  • an embodiment of the present disclosure provides a method for generating a comic style conversion model.
  • the method includes: obtaining a training sample set, wherein the training sample includes a preset sample image and a sample corresponding to the sample image Comic style images; obtain a pre-established generative confrontation network, where the generative confrontation network includes a generation network and a discrimination network.
  • the generation network is used to generate comic style images from the input sample images, and the discrimination network is used to distinguish the comic style output of the generation network
  • the image and the sample comic style image corresponding to the sample image of the input generation network using machine learning methods, the sample image included in the training sample in the training sample set is used as the input of the generation network, and the sample comic style image corresponding to the input sample image
  • the generation network and the discrimination network are trained, and the trained generation network Determined as a manga style conversion model.
  • the discriminant network is a multi-scale discriminator, which is used to discriminate the input image and output at least two discriminant results, wherein, for the discriminant result of the at least two discriminant results output, the discriminant result corresponds to The block image included in the image input to the discrimination network and the sample comic style block image included in the sample comic style image are used to determine whether the block image and the corresponding sample comic style block image match.
  • training the generation network and the discrimination network includes: using a preset loss function to determine the generation loss value that is used to characterize the difference between the comic style image output by the generation network and the corresponding sample comic style image, and Determine the discriminant loss value corresponding to the discriminant network, which is used to characterize the difference between the comic style image actually output by the generation network of the input discriminant network and the sample comic style image; based on the determined generation loss value and discriminant loss value, the generation network is judged The network is trained.
  • the generated loss value is determined by any one of the following loss functions: L1 norm loss function, L2 norm loss function.
  • an embodiment of the present disclosure provides a method for generating a comic style image, the method includes: acquiring a target image; inputting the target image into a pre-trained comic style conversion model to generate a comic style image corresponding to the target image And output, wherein the comic style conversion model is generated according to the method described in any one of the embodiments of the first aspect.
  • the target image is an image frame extracted from the target video.
  • an embodiment of the present disclosure provides an apparatus for generating a comic style conversion model.
  • the apparatus includes: a first acquiring unit configured to acquire a training sample set, wherein the training sample includes a preset sample image , And a sample comic style image corresponding to the sample image; the second acquisition unit is configured to acquire a pre-established generative confrontation network, wherein the generative confrontation network includes a generation network and a discriminant network, and the generation network is used to use the input sample image Generate comic style images, and the discrimination network is used to distinguish between the comic style images output by the generation network and the sample comic style images corresponding to the sample images of the input generation network; the training unit is configured to use machine learning methods to combine the training samples in the training sample set
  • the included sample image is used as the input of the generation network, the sample comic style image corresponding to the input sample image is used as the expected output of the generation network, and the comic style image actually output by the generation network and the sample comic style corresponding to the input sample image
  • the image is used as the input of the discrimin
  • the discriminant network is a multi-scale discriminator, which is used to discriminate the input image and output at least two discriminant results, wherein, for the discriminant result of the at least two discriminant results output, the discriminant result corresponds to The block image included in the image input to the discrimination network and the sample comic style block image included in the sample comic style image are used to determine whether the block image and the corresponding sample comic style block image match.
  • the training unit includes: a determination module configured to use a preset loss function to determine a generation loss value that is used to characterize the difference between the comic style image output by the generation network and the corresponding sample comic style image, and determine The discriminant loss value corresponding to the discriminant network and used to characterize the difference between the comic style image actually output by the generation network of the input discriminant network and the sample comic style image; the training module is configured to be based on the determined generation loss value and discriminant loss value, Train the generation network and the discriminant network.
  • the generated loss value is determined by any one of the following loss functions: L1 norm loss function, L2 norm loss function.
  • an embodiment of the present disclosure provides an apparatus for generating a comic style image.
  • the apparatus includes: an image acquisition unit configured to acquire a target image; an image generation unit configured to input the target image into pre-training
  • the comic style conversion model generates and outputs a comic style image corresponding to the target image, where the comic style conversion model is generated according to the method described in any one of the embodiments of the first aspect.
  • the target image is an image frame extracted from the target video.
  • the embodiments of the present disclosure provide an electronic device that includes: one or more processors; a storage device on which one or more programs are stored; when one or more programs are Multiple processors execute, so that one or more processors implement the method described in any one of the first aspect or the second aspect.
  • the embodiments of the present disclosure provide a computer-readable medium on which a computer program is stored, and when the computer program is executed by a processor, the method described in any one of the first aspect or the second aspect is implemented .
  • the method and device for generating a comic style conversion model provided by the embodiments of the present disclosure are obtained by acquiring a training sample set and a pre-established generating confrontation network, and using a machine learning method, the sample image included in the training sample in the training sample set is taken as The input of the generation network, the sample comic style image corresponding to the input sample image is used as the expected output of the generation network, and the comic style image actually output by the generation network and the sample comic style image corresponding to the input sample image are used as the judgment network Input, train the generation network and the discriminant network, and determine the trained generation network as the comic style conversion model.
  • the training of the generative adversarial network including the generative network and the discriminant network is added, which helps to reduce the generation of the model trained by the supervised training method.
  • Over-fitting problem improve the generalization ability of the model, and improve the detailed processing ability of image style conversion.
  • Using the cartoon style conversion model obtained by training can reduce the image edge jagged and image contour generated by the generated comic style image relative to the original image Deformation and other issues, thereby improving the display effect of the generated comic style image.
  • Fig. 1 is an exemplary system architecture diagram in which an embodiment of the present disclosure can be applied;
  • FIG. 2 is a flowchart of an embodiment of a method for generating a comic style conversion model according to an embodiment of the present disclosure
  • FIG. 3 is a schematic diagram of an application scene of the method for generating a comic style conversion model according to an embodiment of the present disclosure
  • FIG. 4 is a flowchart of an embodiment of a method for generating a comic style image according to an embodiment of the present disclosure
  • Fig. 5 is a schematic structural diagram of an embodiment of an apparatus for generating a comic style conversion model according to an embodiment of the present disclosure
  • FIG. 6 is a schematic structural diagram of an embodiment of an apparatus for generating comic style images according to an embodiment of the present disclosure
  • FIG. 7 is a schematic structural diagram of an electronic device suitable for implementing embodiments of the present disclosure.
  • FIG. 1 shows a method for generating a comic style conversion model or an apparatus for generating a comic style conversion model to which an embodiment of the present disclosure can be applied, and a method for generating a comic style image or a method for generating a comic style image
  • the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105.
  • the network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105.
  • the network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables.
  • the user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, and so on.
  • Various communication client applications such as image processing applications, web browser applications, instant messaging tools, and social platform software, may be installed on the terminal devices 101, 102, and 103.
  • the terminal devices 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices. When the terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices. It can be implemented as multiple software or software modules (for example, software or software modules used to provide distributed services), or as a single software or software module. No specific restrictions are made here.
  • the server 105 may be a server that provides various services, for example, a back-end server that processes a collection of training samples uploaded by the terminal devices 101, 102, and 103.
  • the background server can use the acquired training sample set to train the generative adversarial network to obtain a comic style conversion model.
  • the background server can also use the comic style conversion model to process the input image to obtain the comic style image and output.
  • the method for generating a comic style conversion model provided by the embodiments of the present disclosure can be executed by the server 105, or can be executed by the terminal devices 101, 102, 103, and accordingly, used to generate the comic style conversion model
  • the device of can be set in the server 105 or in the terminal equipment 101, 102, 103.
  • the method for generating comic style images provided by the embodiments of the present disclosure can be executed by the server 105, and can also be executed by the terminal devices 101, 102, 103.
  • the device for generating comic style images can be set in The server 105 may also be provided in the terminal devices 101, 102, 103.
  • the server can be hardware or software.
  • the server can be implemented as a distributed server cluster composed of multiple servers, or as a single server.
  • the server is software, it can be implemented as multiple software or software modules (for example, software or software modules for providing distributed services), or as a single software or software module. No specific restrictions are made here.
  • terminal devices, networks, and servers in FIG. 1 are merely illustrative. According to implementation needs, there can be any number of terminal devices, networks and servers.
  • the foregoing system architecture may not include a network, but only a server or terminal device.
  • the method for generating a comic style conversion model includes the following steps:
  • Step 201 Obtain a training sample set.
  • the executor of the method for generating a comic style conversion model can obtain the training sample collection remotely or locally through a wired connection or a wireless connection.
  • the training samples include preset sample images and sample comic style images corresponding to the sample images.
  • the sample image may be an image obtained by photographing a real person, object, scene, etc.
  • the sample comic style image may be an image obtained by performing various processings on its corresponding sample image (for example, changing colors, shapes, and adding special effects to certain areas in the image), and the resulting image usually has a comic style.
  • the character image, background image, etc. in the sample image are processed such as changing colors and adding lines to make them have a comic style.
  • the corresponding relationship between the sample image and the sample comic style image is established in advance.
  • the technician may process the sample image in advance for each sample image in the plurality of sample images, so as to determine the image obtained after the processing as the sample comic style image corresponding to the image.
  • Step 202 Obtain a pre-established generative confrontation network.
  • the above-mentioned execution subject may obtain a pre-established generational confrontation network locally or remotely.
  • the generative confrontation network includes the generative network and the discriminant network.
  • the generation network is used to generate a comic style image using the input sample image
  • the discrimination network is used to distinguish the comic style image output by the generation network and the sample comic style image corresponding to the sample image input to the generation network.
  • the above-mentioned generative confrontation network may be a generative confrontation network of various structures.
  • the generative confrontation network may be a deep convolutional generation confrontation network (Deep Convolutional Generative Adversarial Network, DCGAN). It should be understood that the above-mentioned generative confrontation network may be an untrained generative confrontation network after initializing parameters, or a trained generative confrontation network.
  • the generating network may be a convolutional neural network used for image processing (for example, a convolutional neural network with various structures including a convolution layer, a pooling layer, a depooling layer, and a deconvolution layer).
  • the above-mentioned discriminant network may be a convolutional neural network (for example, a convolutional neural network of various structures including a fully connected layer, where the above-mentioned fully connected layer can implement a classification function).
  • the discriminant network may also be other models for implementing classification functions, such as Support Vector Machine (SVM).
  • SVM Support Vector Machine
  • the discrimination network determines that the image is the image output by the generation network, it can output the label 1 (or 0) corresponding to the image; if it is determined that the image is not output by the generation network , You can output the label 0 (or 1) corresponding to the image. It should be noted that the discrimination network can also output other preset information, which is not limited to the values 1 and 0.
  • Step 203 Using a machine learning method, the sample images included in the training samples in the training sample set are used as the input of the generation network, the sample comic style images corresponding to the input sample images are used as the expected output of the generation network, and the actual network is generated.
  • the output comic style image and the sample comic style image corresponding to the input sample image are used as the input of the discrimination network, the generation network and the discrimination network are trained, and the trained generation network is determined as the comic style conversion model.
  • the above-mentioned execution subject can use a machine learning method to use the sample images included in the training samples in the training sample set as the input of the generation network, and use the sample comic style images corresponding to the input sample images as the expectations of the generation network.
  • the output, and the comic style image actually output by the generation network and the sample comic style image corresponding to the input sample image are used as the input of the discrimination network, the generation network and the discrimination network are trained, and the trained generation network is determined as a comic style conversion model.
  • the above-mentioned executive body may first fix the parameters of any one of the generation network and the discrimination network (may be called the first network), and optimize the network with no fixed parameters (may be called the second network); then fix it The parameters of the second network are optimized for the first network. Continuously carry out the above iterations, so that the judgment network cannot distinguish whether the input image is generated by the generation network.
  • the comic style image generated by the generation network is close to the sample comic style image, and the discrimination network cannot accurately distinguish the comic style image generated by the generation network from the sample comic style image (that is, the discrimination accuracy rate is 50%).
  • the generation network is determined to be a comic style conversion model.
  • the above-mentioned executive body can use the existing back propagation algorithm and gradient descent algorithm to train the generation network and the discriminant network.
  • the parameters of the generative network and the discriminant network after each training will be adjusted, and the generative network and the discriminant network obtained after each parameter adjustment are used as the generative confrontation network used in the next training.
  • the loss value can be determined by using the loss function, and the generation network and the discriminant network are iteratively trained according to the loss value to minimize the loss value determined during each iteration.
  • the method adopted in this embodiment is to use the sample images included in the training samples in the training sample set as the input of the generation network, and use the sample comic style image corresponding to the input sample image As the expected output, the generation network is trained, and the actual output can be obtained for the sample image input for each training. Among them, the actual output is the comic style image actually output by the generated network. Then, the above-mentioned execution body can adopt the back propagation algorithm and the gradient descent algorithm to adjust the parameters of the generation network based on the actual output and the expected output, and use the generation network after each adjustment of the parameters as the generation network used for the next training.
  • the above-mentioned execution subject may train the generation network and the discrimination network according to the following steps:
  • the generation loss value used to characterize the difference between the comic style image output by the generation network and the corresponding sample comic style image ie, the sample comic style image corresponding to the input sample image
  • the generated loss value can be the loss value determined according to the regression loss function.
  • the regression loss function is generally expressed as L(y, y′), and the loss value obtained by it is used to represent the true value (that is, the loss value in this embodiment) The degree of inconsistency between the sample image) y and the predicted value (that is, the comic style image output by the generation network in this embodiment) y′.
  • the generation loss value is minimized.
  • a loss function for two classification for example, a cross-entropy loss function
  • the generated loss value may be determined by any one of the following loss functions: L1 norm loss function, L2 norm loss function.
  • the L1 norm loss function and the L2 norm loss function are the existing pixel-level loss functions, that is, the pixel is the basic unit to determine the difference between the pixels included in the two images, which can improve the use of the generated loss value characterization The accuracy of the differences between the images.
  • the generation network and the discrimination network are trained. Specifically, the preset weights corresponding to the generated loss value and the discriminated loss value can be used to perform a weighted summation on the determined loss values to obtain the total loss value.
  • the preset condition for example, less than or equal to the preset loss value threshold, or the total loss value no longer decreases
  • the discrimination network may be a multi-scale discriminator, which is used to discriminate the input image and output at least two discrimination results.
  • the discrimination result corresponds to the block image included in the comic style image input to the discrimination network and the sample comic style block image included in the sample comic style image, the discrimination result Used to determine whether the block image matches the corresponding sample comic style block image.
  • the comic style image input to the judgment network is the comic style image output by the generated network, and the divided image included is obtained by dividing it.
  • the sample comic style segmented image included in the sample comic style image is obtained by dividing the sample comic style image.
  • each discrimination result corresponds to a block image and a sample comic style block image, and the corresponding relationship between the discrimination result and the block image and the sample comic block image is preset.
  • the aforementioned at least two discrimination results may be a matrix with N rows and N columns, where N is a preset positive integer. Each element in the matrix is the result of discrimination.
  • Each discrimination result corresponds to a block image and a sample comic block image.
  • the discrimination network separately divides the input comic style image and the sample comic style image to obtain N ⁇ N block images and N ⁇ N comic style block images. There is a one-to-one correspondence between the block image and the comic style block image.
  • the discriminant network sequentially discriminates the corresponding block image and the comic style block image, and obtains the corresponding discrimination result.
  • the discrimination result can be the number 0 or 1. 0 can indicate that the corresponding block image does not match the comic style block image, and 1 can indicate that the corresponding block image matches the comic style block image.
  • the discrimination accuracy rate is 50%).
  • the above-mentioned multi-scale discriminator may be a convolutional neural network with a PatchGAN structure.
  • PatchGAN can effectively identify high-frequency components in an image and improve the accuracy of discriminating details in the image. This helps to improve the accuracy of judging the comic style image and the sample comic style image generated by the generation network.
  • FIG. 3 is a schematic diagram of an application scenario of the method for generating a comic style conversion model according to the present embodiment.
  • the electronic device 301 first obtains the training sample set 302 locally.
  • each training sample in the training sample set 302 includes a preset sample image and a sample comic style image corresponding to the sample image.
  • the sample comic image is the image obtained by the technicians who change the color, shape, and add special effects of the sample image in advance.
  • the electronic device 301 obtains the pre-established generation confrontation network 303 locally.
  • the generation confrontation network 303 includes a generation network 3031 and a discrimination network 3032.
  • the generation network 3031 is used to generate a comic style image using input sample images, and the discrimination network 3032 is used to determine whether the image input to the discrimination network is an image output by the generation network. Then, using a machine learning method, the sample images included in the training samples in the training sample set 302 are used as the input of the generation network, the sample comic style image corresponding to the input sample image is used as the expected output of the generation network, and the generation network The actual output comic style image and the sample comic style image corresponding to the input sample image are used as the input of the discrimination network, and the generation network and the discrimination network are trained.
  • the comic style image output by the generation network is compared with the corresponding sample comic style image, and the judgment result of the comic style image output by the generation network and the corresponding sample comic style image through the discrimination network , Adjust the parameters of the generated network.
  • the discrimination network cannot accurately distinguish the comic style image output by the generation network and the sample comic style image (that is, the discrimination accuracy rate is 50%)
  • the generation network at this time is determined as the comic style conversion model 304.
  • the method provided by the above-mentioned embodiments of the present disclosure obtains a training sample set and a pre-established generative confrontation network, and uses a machine learning method to use the sample image included in the training sample in the training sample set as the input of the generative network, which will be compared with the input
  • the sample comic style image corresponding to the sample image is used as the expected output of the generation network, and the comic style image actually output by the generation network and the sample comic style image corresponding to the input sample image are used as the input of the discrimination network, and the generation network and the discrimination network are processed Training, the generated network after training is determined as a comic style conversion model.
  • the training of the generative adversarial network including the generative network and the discriminant network is added, which helps to reduce the generation of the model trained by the supervised training method.
  • Over-fitting problem improve the generalization ability of the model, and improve the detail processing ability of image style conversion.
  • Using the manga style conversion model obtained by training can reduce the jagged edges of the generated manga style image relative to the original image and reduce the image Contour distortion and other issues, thereby improving the display effect of the generated comic style image.
  • FIG. 4 shows a process 400 of an embodiment of a method for generating a comic style image according to the present disclosure.
  • the process 400 of the method for generating comic style images includes the following steps:
  • Step 401 Obtain a target image.
  • the execution subject of the method for generating comic style images may obtain the target image remotely or locally through a wired connection or a wireless connection.
  • the target image is an image to be used to generate a comic style image.
  • the target image may be an image obtained by photographing a target object by a camera included in the above-mentioned execution subject or a camera included in an electronic device communicatively connected with the above-mentioned execution subject, and the target object may be a person, thing, or scene within the shooting range of the camera. Wait.
  • the foregoing target image is an image frame extracted from the target video.
  • the target video may be a video whose image frames are to be converted into comic style images.
  • the target video may be a video currently played on the execution subject, or a video currently being shot by a camera included in the execution subject or an electronic device communicatively connected with the execution subject.
  • the above-mentioned execution master may extract an image frame from the target image as the target image.
  • the target image may be an image frame included in the target video and currently displayed on the display screen included in the execution subject.
  • Step 402 Input the target image into a pre-trained comic style conversion model to generate and output a comic style image corresponding to the target image.
  • the above-mentioned execution subject may input the target image into a pre-trained comic style conversion model to generate and output a comic style image corresponding to the target image.
  • the comic style conversion model is generated according to the method described in the embodiment corresponding to FIG. 2 above.
  • the above-mentioned execution subject can output the generated comic style image in various ways.
  • the generated comic style image may be displayed on a display screen included with the execution subject, or the generated comic style image may be sent to other electronic devices communicatively connected with the execution subject.
  • the method provided in the above-mentioned embodiment of the present disclosure obtains a target image, inputs the target image into a comic style conversion model trained in advance according to the method described in the above-mentioned embodiment corresponding to FIG. 2 to generate a comic-style image corresponding to the target image and output it, using this
  • the comic style conversion model can reduce the over-fitting problem of generating comic style images, and improve the effect of comic style conversion on the details in the image, and can reduce the image edge jagged of the generated comic style image relative to the original image. Image contour distortion and other issues, thereby improving the display effect of the generated comic style image.
  • the present disclosure provides an embodiment of a device for generating a comic style conversion model, which is similar to the method embodiment shown in FIG.
  • the device can be specifically applied to various electronic devices.
  • the apparatus 500 for generating a comic style conversion model of this embodiment includes: a first obtaining unit 501 configured to obtain a set of training samples, where the training samples include preset sample images and A sample comic style image corresponding to the image; the second acquisition unit 502 is configured to acquire a pre-established generative confrontation network, wherein the generation of the confrontation network includes a generation network and a discrimination network, and the generation network is used to generate a comic style using the input sample image
  • the discriminant network is used to distinguish the comic style image output by the generation network and the sample comic style image corresponding to the sample image of the input generation network;
  • the training unit 503 is configured to use a machine learning method to include the training samples in the training sample set
  • the sample image is used as the input of the generation network, the sample comic style image corresponding to the input sample image is used as the expected output of the generation network, and the comic style image actually output by the generation network and the sample comic style image corresponding to the input sample image are used as The input of the discriminant network is trained, the generative
  • the first obtaining unit 501 may obtain the training sample set remotely or locally through a wired connection or a wireless connection.
  • the training samples include preset sample images and sample comic style images corresponding to the sample images.
  • the sample image may be an image obtained by photographing a real person, object, scene, etc.
  • the sample comic style image may be an image obtained by performing various processings on its corresponding sample image (for example, changing colors, shapes, and adding special effects to certain areas in the image), and the resulting image usually has a comic style.
  • the character image, background image, etc. in the sample image are processed such as changing colors and adding lines to make them have a comic style.
  • the corresponding relationship between the sample image and the sample comic style image is established in advance.
  • the technician may process the sample image in advance for each sample image in the plurality of sample images, so as to determine the image obtained after the processing as the sample comic style image corresponding to the image.
  • the second acquiring unit 502 may acquire a pre-established generative confrontation network locally or remotely.
  • the generative confrontation network includes the generative network and the discriminant network.
  • the generation network is used to generate a comic style image using the input sample image
  • the discrimination network is used to distinguish the comic style image output by the generation network and the sample comic style image corresponding to the sample image input to the generation network.
  • the above-mentioned generative confrontation network may be a generative confrontation network of various structures.
  • the generative confrontation network may be a deep convolutional generation confrontation network (Deep Convolutional Generative Adversarial Network, DCGAN). It should be understood that the above-mentioned generative confrontation network may be an untrained generative confrontation network after initializing parameters, or a trained generative confrontation network.
  • the generating network may be a convolutional neural network used for image processing (for example, a convolutional neural network with various structures including a convolution layer, a pooling layer, a depooling layer, and a deconvolution layer).
  • the above-mentioned discriminant network may be a convolutional neural network (for example, a convolutional neural network of various structures including a fully connected layer, where the above-mentioned fully connected layer can implement a classification function).
  • the discriminant network may also be other models for implementing classification functions, such as Support Vector Machine (SVM).
  • SVM Support Vector Machine
  • the discrimination network determines that the image is the image output by the generation network, it can output the label 1 (or 0) corresponding to the image; if it is determined that the image is not output by the generation network , You can output the label 0 (or 1) corresponding to the image. It should be noted that the discrimination network can also output other preset information, which is not limited to the values 1 and 0.
  • the training unit 503 can use a machine learning method to use the sample images included in the training samples in the training sample set as the input to the generation network, and use the sample comic style images corresponding to the input sample images as the expectations of the generation network. Output, and use the comic style image actually output by the generation network and the sample comic style image corresponding to the input sample image as the input of the discrimination network, train the generation network and the discrimination network, and determine the generated network after training as the comic style conversion model.
  • the above-mentioned training unit 503 may first fix the parameters of any one of the generation network and the discrimination network (may be called the first network), and optimize the network without fixed parameters (may be called the second network); and then Fix the parameters of the second network and optimize the first network. Continuously carry out the above iterations, so that the judgment network cannot distinguish whether the input image is generated by the generation network.
  • the comic style image generated by the generation network is close to the sample comic style image, and the discrimination network cannot accurately distinguish the comic style image generated by the generation network from the sample comic style image (that is, the discrimination accuracy rate is 50%).
  • the generation network is determined to be a comic style conversion model.
  • the above-mentioned training unit 503 can use the existing back propagation algorithm and gradient descent algorithm to train the generation network and the discrimination network.
  • the parameters of the generative network and the discriminant network after each training will be adjusted, and the generative network and the discriminant network obtained after each parameter adjustment are used as the generative confrontation network used in the next training.
  • the loss value can be determined by using the loss function, and the generation network and the discriminant network are iteratively trained according to the loss value to minimize the loss value determined during each iteration.
  • the method adopted in this embodiment is to use the sample images included in the training samples in the training sample set as the input of the generation network, and use the sample comic style image corresponding to the input sample image As the expected output, the generation network is trained, and the actual output can be obtained for the sample image input for each training. Among them, the actual output is the comic style image actually output by the generated network.
  • the above-mentioned training unit 503 may adopt a back propagation algorithm and a gradient descent algorithm to adjust the parameters of the generation network based on the actual output and the expected output, and use the generation network after each adjustment of the parameters as the generation network used for the next training.
  • the discriminant network is a multi-scale discriminator, used to discriminate the input image, and output at least two discriminating results, wherein, for the at least two discriminating results output
  • the discrimination result which corresponds to the block image included in the image input to the discrimination network and the sample comic style block image included in the sample comic style image, is used to determine whether the block image matches the corresponding sample comic style block image.
  • the training unit 503 may include: a determining module (not shown in the figure), configured to use a preset loss function to determine the comic style image and the corresponding sample comic style image used to characterize the network output The generation loss value of the difference of the discriminant network, and the discriminant loss value that determines the difference between the comic style image and the sample comic style image corresponding to the discriminant network and used to represent the actual output of the generation network of the input discriminant network; training module (not shown in the figure) , Is configured to train the generation network and the discrimination network based on the determined generation loss value and the discrimination loss value.
  • the generated loss value is determined by any one of the following loss functions: L1 norm loss function, L2 norm loss function.
  • the apparatus 500 acquires a training sample set and a pre-established generative confrontation network, and uses a machine learning method to use a sample image included in the training sample in the training sample set as the input of the generative network, and will be compared with the input
  • the sample comic style image corresponding to the sample image of the generation network is used as the expected output of the generation network, and the comic style image actually output by the generation network and the sample comic style image corresponding to the input sample image are used as the input of the discrimination network, and the generation network and the discrimination network Perform training and determine the trained generation network as a comic style conversion model.
  • the training of the generative adversarial network including the generative network and the discriminant network is added, which helps to reduce the generation of the model trained by the supervised training method.
  • Over-fitting problem improve the generalization ability of the model, and improve the detail processing ability of image style conversion.
  • Using the manga style conversion model obtained by training can reduce the jagged edges of the generated manga style image relative to the original image and reduce the image Contour distortion and other issues, thereby improving the display effect of the generated comic style image.
  • the present disclosure provides an embodiment of a device for generating comic style images, and the device embodiment corresponds to the method embodiment shown in FIG. 4 ,
  • the device can be applied to various electronic equipment.
  • the apparatus 600 for generating a comic style image of this embodiment includes: an image acquisition unit 601 configured to acquire a target image; an image generation unit 602 configured to input the target image into a pre-trained comic style
  • the conversion model generates and outputs a comic style image corresponding to the target image.
  • the comic style conversion model is generated according to the method described in the embodiment corresponding to FIG. 2 above.
  • the image acquisition unit 601 can acquire the target image remotely or locally through a wired connection or a wireless connection.
  • the target image is an image to be used to generate a comic style image.
  • the target image may be an image obtained by photographing a target object by a camera included in the foregoing apparatus 600 or an electronic device included in an electronic device communicatively connected with the foregoing apparatus 600, and the target object may be a person, thing, or scene within the shooting range of the camera. Wait.
  • the image generating unit 602 may input the target image into a pre-trained comic style conversion model to generate and output a comic style image corresponding to the target image.
  • the comic style conversion model is generated according to the method described in the embodiment corresponding to FIG. 2 above.
  • the above-mentioned image generating unit 602 can output the generated comic style image in various ways.
  • the generated comic style image may be displayed on a display screen included with the execution subject, or the generated comic style image may be sent to other electronic devices communicatively connected with the execution subject.
  • the target image is an image frame extracted from the target video.
  • the apparatus 600 provided in the above-mentioned embodiment of the present disclosure acquires a target image, inputs the target image into the comic style conversion model trained in advance according to the method described in the above-mentioned embodiment corresponding to FIG. 2, and generates and outputs the comic style image corresponding to the target image.
  • the comic style conversion model can reduce the over-fitting problem of generating comic style images, improve the effect of comic style conversion on details in the image, and reduce the jagged edges of the generated comic style image relative to the original image. Reduce the problem of image contour distortion, thereby improving the display effect of the generated comic style image.
  • FIG. 7 shows a schematic structural diagram of an electronic device (such as the server or terminal device in FIG. 1) 700 suitable for implementing the embodiments of the present disclosure.
  • Terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablets), PMPs (portable multimedia players), vehicle-mounted terminals ( For example, mobile terminals such as car navigation terminals and fixed terminals such as digital TVs and desktop computers.
  • the electronic device shown in FIG. 7 is only an example, and should not bring any limitation to the function and scope of use of the embodiments of the present disclosure.
  • the electronic device 700 may include a processing device (such as a central processing unit, a graphics processor, etc.) 701, which may be loaded into a random access device according to a program stored in a read-only memory (ROM) 702 or from a storage device 708.
  • the program in the memory (RAM) 703 executes various appropriate actions and processing.
  • various programs and data required for the operation of the electronic device 700 are also stored.
  • the processing device 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704.
  • An input/output (I/O) interface 705 is also connected to the bus 704.
  • the following devices can be connected to the I/O interface 705: including input devices 706 such as touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; including, for example, liquid crystal display (LCD), speakers, vibration An output device 707 such as a device; a storage device 708 such as a magnetic tape and a hard disk; and a communication device 709.
  • the communication device 709 may allow the electronic device 700 to perform wireless or wired communication with other devices to exchange data.
  • FIG. 7 shows an electronic device 700 having various devices, it should be understood that it is not required to implement or have all the illustrated devices. It may alternatively be implemented or provided with more or fewer devices. Each block shown in FIG. 7 may represent one device, or may represent multiple devices as needed.
  • the process described above with reference to the flowchart can be implemented as a computer software program.
  • the embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart.
  • the computer program may be downloaded and installed from the network through the communication device 709, or installed from the storage device 708, or installed from the ROM 702.
  • the processing device 701 the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.
  • the computer-readable medium described in the embodiment of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.
  • the computer-readable signal medium may include a data signal propagated in a baseband or as a part of a carrier wave, and a computer-readable program code is carried therein. This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • the computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium.
  • the computer-readable signal medium may send, propagate, or transmit the program for use by or in combination with the instruction execution system, apparatus, or device .
  • the program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wire, optical cable, RF (Radio Frequency), etc., or any suitable combination of the above.
  • the above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist alone without being assembled into the electronic device.
  • the above-mentioned computer-readable medium carries one or more programs.
  • the electronic device is caused to: obtain a training sample set, wherein the training sample includes a preset sample image, and The sample comic style image corresponding to the sample image; obtain a pre-established generative confrontation network, where the generative confrontation network includes a generation network and a discriminant network.
  • the generation network is used to generate a comic style image using the input sample image
  • the discriminant network is used to differentiate The comic style image output by the network and the sample comic style image corresponding to the sample image of the input generation network; using machine learning methods, the sample images included in the training samples in the training sample set are used as the input of the generation network, which will correspond to the input sample image
  • the sample comic style image of the generation network is used as the expected output of the generation network, and the comic style image actually output by the generation network and the sample comic style image corresponding to the input sample image are used as the input of the discrimination network, and the generation network and the discrimination network are trained.
  • the generated network after training is determined to be the comic style conversion model.
  • the electronic device when the above-mentioned one or more programs are executed by the electronic device, the electronic device is caused to: acquire the target image; input the target image into the pre-trained comic style conversion model to generate and output the comic style image corresponding to the target image.
  • the computer program code used to perform the operations of the embodiments of the present disclosure can be written in one or more programming languages or a combination thereof, the programming languages including object-oriented programming languages such as Java, Smalltalk, C++, It also includes conventional procedural programming languages-such as "C" language or similar programming languages.
  • the program code can be executed entirely on the user's computer, partly on the user's computer, executed as an independent software package, partly on the user's computer and partly executed on a remote computer, or entirely executed on the remote computer or server.
  • the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to pass Internet connection).
  • LAN local area network
  • WAN wide area network
  • each block in the flowchart or block diagram can represent a module, program segment, or part of code, and the module, program segment, or part of code contains one or more for realizing the specified logic function Executable instructions.
  • the functions marked in the block may also occur in a different order from the order marked in the drawings. For example, two blocks shown in succession can actually be executed substantially in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved.
  • each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or operations Or it can be realized by a combination of dedicated hardware and computer instructions.
  • the units involved in the embodiments described in the present disclosure may be implemented in a software manner, and may also be implemented in a hardware manner.
  • the described unit may also be provided in the processor, for example, it may be described as: a processor includes a first acquiring unit, a second acquiring unit, and a training unit. Wherein, the names of these units do not constitute a limitation on the unit itself under certain circumstances.
  • the first obtaining unit can also be described as "a unit for obtaining a training sample set.”

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

L'invention concerne un procédé et un appareil pour la génération d'un modèle de conversion au style cartoon. Un mode de réalisation particulier du procédé consiste à : acquérir un ensemble d'échantillons d'apprentissage ; acquérir un réseau antagoniste génératif préétabli ; et utiliser un procédé d'apprentissage automatique pour prendre des images d'échantillon comprises dans des échantillons d'apprentissage de l'ensemble d'échantillons d'apprentissage en tant que données d'entrée d'un réseau de génération, prendre des images d'échantillon de style cartoon correspondant aux images d'échantillon entrées en tant que données de sortie attendues du réseau de génération et prendre des images de style cartoon effectivement produites par le réseau de génération et les images d'échantillon de style cartoon correspondant aux images d'échantillon entrées en tant que données d'entrée d'un réseau d'évaluation, entraîner le réseau de génération et le réseau d'évaluation et déterminer que le réseau de génération entraîné constitue un modèle de conversion au style cartoon. Selon le mode de réalisation, les problèmes tels que les dents de scie de bord d'image et la déformation des contours de l'image, générés par rapport aux images originales, des images de style cartoon générées peuvent être réduits, de telle sorte que l'effet d'affichage des images de style cartoon générées s'en trouve amélioré.
PCT/CN2019/126379 2019-01-30 2019-12-18 Procédé et appareil pour la génération d'un modèle de conversion au style cartoon WO2020155907A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910090075.6A CN109816589B (zh) 2019-01-30 2019-01-30 用于生成漫画风格转换模型的方法和装置
CN201910090075.6 2019-01-30

Publications (1)

Publication Number Publication Date
WO2020155907A1 true WO2020155907A1 (fr) 2020-08-06

Family

ID=66605948

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/126379 WO2020155907A1 (fr) 2019-01-30 2019-12-18 Procédé et appareil pour la génération d'un modèle de conversion au style cartoon

Country Status (2)

Country Link
CN (1) CN109816589B (fr)
WO (1) WO2020155907A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112330534A (zh) * 2020-11-13 2021-02-05 北京字跳网络技术有限公司 动物脸风格图像生成方法、模型训练方法、装置和设备
CN113393544A (zh) * 2020-09-30 2021-09-14 腾讯科技（深圳）有限公司 一种图像处理方法、装置、设备及介质
CN115100334A (zh) * 2022-08-24 2022-09-23 广州极尚网络技术有限公司 一种图像描边、图像动漫化方法、设备及存储介质

Families Citing this family (27)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109816589B (zh) * 2019-01-30 2020-07-17 北京字节跳动网络技术有限公司 用于生成漫画风格转换模型的方法和装置
CN112446190A (zh) * 2019-08-16 2021-03-05 北京大数医达科技有限公司 生成风格转化文本的方法和装置
CN110458918B (zh) * 2019-08-16 2023-05-09 北京百度网讯科技有限公司 用于输出信息的方法和装置
CN110516201B (zh) * 2019-08-20 2023-03-28 Oppo广东移动通信有限公司 图像处理方法、装置、电子设备及存储介质
CN111047507B (zh) * 2019-11-29 2024-03-26 北京达佳互联信息技术有限公司 图像生成模型的训练方法、图像生成方法及装置
CN111080512B (zh) * 2019-12-13 2023-08-15 咪咕动漫有限公司 动漫图像生成方法、装置、电子设备及存储介质
CN111242844B (zh) * 2020-01-19 2023-09-22 腾讯科技（深圳）有限公司 图像处理方法、装置、服务器和存储介质
CN113139893B (zh) * 2020-01-20 2023-10-03 北京达佳互联信息技术有限公司 图像翻译模型的构建方法和装置、图像翻译方法和装置
CN113259583B (zh) * 2020-02-13 2023-05-12 北京小米移动软件有限公司 一种图像处理方法、装置、终端及存储介质
CN111325786B (zh) * 2020-02-18 2022-06-28 北京市商汤科技开发有限公司 图像处理方法及装置、电子设备和存储介质
CN111402151A (zh) * 2020-03-09 2020-07-10 北京字节跳动网络技术有限公司 图像处理方法、装置、电子设备及计算机可读介质
CN111369468B (zh) * 2020-03-09 2022-02-01 北京字节跳动网络技术有限公司 图像处理方法、装置、电子设备及计算机可读介质
CN111402112A (zh) * 2020-03-09 2020-07-10 北京字节跳动网络技术有限公司 图像处理方法、装置、电子设备及计算机可读介质
CN111553283B (zh) * 2020-04-29 2023-08-25 北京百度网讯科技有限公司 用于生成模型的方法及装置
CN111832609B (zh) * 2020-06-01 2024-02-13 北京百度网讯科技有限公司 图像处理模型的训练方法、装置、电子设备和存储介质
CN111784567B (zh) * 2020-07-03 2023-04-28 北京字节跳动网络技术有限公司 用于转换图像的方法、装置、电子设备和计算机可读介质
CN112991148B (zh) * 2020-10-30 2023-08-11 抖音视界有限公司 风格图像生成方法、模型的训练方法、装置、设备及介质
CN112529058A (zh) * 2020-12-03 2021-03-19 北京百度网讯科技有限公司 图像生成模型训练方法和装置、图像生成方法和装置
CN112967174B (zh) * 2021-01-21 2024-02-09 北京达佳互联信息技术有限公司 图像生成模型训练、图像生成方法、装置及存储介质
CN112862110B (zh) * 2021-02-11 2024-01-30 脸萌有限公司 模型生成方法、装置和电子设备
CN113301268A (zh) * 2021-04-30 2021-08-24 南京大学 基于风格迁移与语音识别的视频自动生成连环画的方法
CN113610989B (zh) * 2021-08-04 2022-12-27 北京百度网讯科技有限公司 风格迁移模型训练方法和装置、风格迁移方法和装置
CN113610731B (zh) * 2021-08-06 2023-08-08 北京百度网讯科技有限公司 用于生成画质提升模型的方法、装置及计算机程序产品
CN113901997A (zh) * 2021-09-29 2022-01-07 北京百度网讯科技有限公司 图像风格转换方法、装置、设备、存储介质及程序产品
CN114067052A (zh) * 2021-11-16 2022-02-18 百果园技术(新加坡)有限公司 漫画化模型构建方法、装置、设备、存储介质及程序产品
CN113837933A (zh) * 2021-11-26 2021-12-24 北京市商汤科技开发有限公司 网络训练及图像生成方法、装置、电子设备和存储介质
CN117576245B (zh) * 2024-01-15 2024-05-07 腾讯科技（深圳）有限公司 一种图像的风格转换方法、装置、电子设备及存储介质

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108550107A (zh) * 2018-04-27 2018-09-18 Oppo广东移动通信有限公司 一种图片处理方法、图片处理装置及移动终端
CN109255769A (zh) * 2018-10-25 2019-01-22 厦门美图之家科技有限公司 图像增强网络的训练方法和训练模型、及图像增强方法
CN109816589A (zh) * 2019-01-30 2019-05-28 北京字节跳动网络技术有限公司 用于生成漫画风格转换模型的方法和装置

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107730573A (zh) * 2017-09-22 2018-02-23 西安交通大学 一种基于特征提取的人物肖像漫画风格化生成方法
CN108491809B (zh) * 2018-03-28 2023-09-22 百度在线网络技术（北京）有限公司 用于生成近红外图像生成模型的方法和装置
CN108491823B (zh) * 2018-03-30 2021-12-24 百度在线网络技术（北京）有限公司 用于生成人眼识别模型的方法和装置

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108550107A (zh) * 2018-04-27 2018-09-18 Oppo广东移动通信有限公司 一种图片处理方法、图片处理装置及移动终端
CN109255769A (zh) * 2018-10-25 2019-01-22 厦门美图之家科技有限公司 图像增强网络的训练方法和训练模型、及图像增强方法
CN109816589A (zh) * 2019-01-30 2019-05-28 北京字节跳动网络技术有限公司 用于生成漫画风格转换模型的方法和装置

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113393544A (zh) * 2020-09-30 2021-09-14 腾讯科技（深圳）有限公司 一种图像处理方法、装置、设备及介质
CN112330534A (zh) * 2020-11-13 2021-02-05 北京字跳网络技术有限公司 动物脸风格图像生成方法、模型训练方法、装置和设备
CN115100334A (zh) * 2022-08-24 2022-09-23 广州极尚网络技术有限公司 一种图像描边、图像动漫化方法、设备及存储介质
CN115100334B (zh) * 2022-08-24 2022-11-25 广州极尚网络技术有限公司 一种图像描边、图像动漫化方法、设备及存储介质

Also Published As

Publication number Publication date
CN109816589A (zh) 2019-05-28
CN109816589B (zh) 2020-07-17

Similar Documents

Publication Publication Date Title
WO2020155907A1 (fr) Procédé et appareil pour la génération d'un modèle de conversion au style cartoon
CN109800732B (zh) 用于生成漫画头像生成模型的方法和装置
CN108520220B (zh) 模型生成方法和装置
CN108427939B (zh) 模型生成方法和装置
WO2020006961A1 (fr) Procédé et dispositif d'extraction d'image
CN111476871B (zh) 用于生成视频的方法和装置
CN109740018B (zh) 用于生成视频标签模型的方法和装置
CN108197652B (zh) 用于生成信息的方法和装置
CN110021052B (zh) 用于生成眼底图像生成模型的方法和装置
CN109800730B (zh) 用于生成头像生成模型的方法和装置
WO2020207174A1 (fr) Procédé et appareil de génération de réseau neuronal quantifié
WO2020211573A1 (fr) Procédé et dispositif de traitement d'image
CN112149699B (zh) 用于生成模型的方法、装置和用于识别图像的方法、装置
WO2020093724A1 (fr) Procédé et dispositif de production d'informations
WO2023005386A1 (fr) Procédé et appareil d'entraînement de modèle
WO2020238321A1 (fr) Procédé et dispositif d'identification d'âge
CN111311480A (zh) 图像融合方法和装置
CN113449851A (zh) 数据处理方法及设备
CN111312223B (zh) 语音分割模型的训练方法、装置和电子设备
CN111539287B (zh) 训练人脸图像生成模型的方法和装置
CN114898177B (zh) 缺陷图像生成方法、模型训练方法、设备、介质及产品
CN109816023B (zh) 用于生成图片标签模型的方法和装置
CN112241761B (zh) 模型训练方法、装置和电子设备
CN113409307A (zh) 基于异质噪声特性的图像去噪方法、设备及介质
WO2021012691A1 (fr) Procédé et dispositif de récupération d'image

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19913736

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 01.12.2021)

122 Ep: pct application non-entry in european phase

Ref document number: 19913736

Country of ref document: EP

Kind code of ref document: A1