WO2023019908A1 - Procédé et appareil pour générer un ensemble d'échantillons d'apprentissage, et dispositif électronique, support de stockage et programme - Google Patents

Procédé et appareil pour générer un ensemble d'échantillons d'apprentissage, et dispositif électronique, support de stockage et programme Download PDF

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WO2023019908A1
WO2023019908A1 PCT/CN2022/078350 CN2022078350W WO2023019908A1 WO 2023019908 A1 WO2023019908 A1 WO 2023019908A1 CN 2022078350 W CN2022078350 W CN 2022078350W WO 2023019908 A1 WO2023019908 A1 WO 2023019908A1
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unlabeled
samples
neural network
sample
value
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Chinese (zh)
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钟华平
刘卓名
何聪辉
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上海商汤智能科技有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • 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
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Definitions

  • the present disclosure relates to the technical field of machine learning, and in particular to a method, device, electronic device and storage medium for generating a training sample set.
  • the training data set is a data set with rich annotation information. Collecting and annotating such a data set usually requires huge manpower and material resources.
  • the training data when collecting and labeling the training data set, the training data can be screened manually to construct a better data set, which leads to high manpower and material cost.
  • Embodiments of the present disclosure at least provide a method, device, electronic device, storage medium, and program for generating a training sample set, so as to automatically select training samples, saving time and effort.
  • An embodiment of the present disclosure provides a method for generating a training sample set, the method comprising:
  • the method of generating the training sample set above in the case of obtaining each unlabeled sample and the target neural network, it is possible to first determine the estimated influence degree value of each unlabeled sample on the network training of the target neural network, and then from each unlabeled sample The target unlabeled samples whose estimated impact value meets the preset requirements are selected from the labeled samples. In this way, after the target unlabeled samples are labeled, the target labeled samples can be obtained to update the training sample set.
  • the disclosure realizes the automatic selection of unlabeled samples based on the estimated influence degree value, which is more time-saving and labor-saving than the manual selection scheme, and also reduces the subsequent labeling cost.
  • An embodiment of the present disclosure also provides a device for generating a training sample set, the device comprising:
  • An acquisition module configured to acquire each unlabeled sample and the target neural network trained based on the training sample set
  • the determination module is configured to determine the estimated influence degree value of each unlabeled sample on the network training of the target neural network based on the respective unlabeled samples and the target neural network;
  • the selection module is configured to select target unlabeled samples whose estimated influence degree value meets the preset requirements from each of the unlabeled samples;
  • the generation module is configured to add the target labeled samples to the training sample set to obtain an updated training sample set when the target unlabeled sample is sample labeled to obtain the target labeled sample;
  • the updated training sample set is used to perform network training on the target neural network again.
  • An embodiment of the present disclosure also provides an electronic device, including: a processor, a memory, and a bus.
  • the memory stores machine-readable instructions executable by the processor.
  • the processor and the The memory communicates with each other through a bus, and when the machine-readable instructions are executed by the processor, the steps of the method for generating a training sample set described in any embodiment are executed.
  • An embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for generating a training sample set described in any embodiment is executed. step.
  • An embodiment of the present disclosure further provides a computer program, where the computer program includes computer readable codes, and when the computer readable codes run in an electronic device, a processor of the electronic device executes the program described in any embodiment. Steps of the method for generating the training sample set described above.
  • FIG. 1 shows a schematic flowchart of a method for generating a training sample set provided by an embodiment of the present disclosure
  • FIG. 2 shows a schematic diagram of a system architecture that can be applied to a method for generating a training sample set according to an embodiment of the present disclosure
  • FIG. 3 shows a schematic structural diagram of an apparatus for generating a training sample set provided by an embodiment of the present disclosure
  • Fig. 4 shows a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.
  • the related technology also provides an active learning training data screening method, which considers the uncertainty of the model for unlabeled samples, or the impact of unlabeled samples on the diversity of labeled datasets. Measure the importance of samples, which can automatically find high-value data in unlabeled samples. Compared with manual operation, the above method only takes a fraction of the time to build a better dataset, and uses very little data to train an efficient model, thereby reducing the cost of labeling.
  • Uncertainty-based active learning algorithms and diversity-based active learning algorithms are two mainstream algorithms in related technologies, but both have their own defects.
  • Uncertainty-based methods Because neural networks often exhibit overconfidence in unfamiliar samples, such methods choose samples inaccurately.
  • Methods based on sample diversity The current state of the model is not considered to select samples, and the computational complexity is usually proportional to the square of the size of the data set.
  • the embodiments of the present disclosure provide a method, device, electronic device, storage medium and program for generating a training sample set, so as to automatically realize the selection of training samples, saving time and effort.
  • the execution subject of the method for generating a training sample set provided by the embodiment of the present disclosure generally has a certain computing power
  • the electronic equipment includes, for example: terminal equipment or server or other processing equipment
  • the terminal equipment can be user equipment (User Equipment, UE), mobile equipment, user terminal, cellular phone, cordless phone, personal digital assistant (Personal Digital Assistant) Assistant, PDA), handheld devices, computing devices, vehicle-mounted devices, wearable devices, etc.
  • the method for generating a training sample set may be implemented by a processor invoking computer-readable instructions stored in a memory.
  • FIG. 1 it is a schematic flowchart of a method for generating a training sample set provided by an embodiment of the present disclosure, the method is executed by an electronic device, and the method includes steps S101 to S104, wherein:
  • S102 Based on each unlabeled sample and the target neural network, determine the estimated influence degree value of each unlabeled sample on the network training of the target neural network;
  • S104 In the case of sample labeling the selected target unlabeled sample to obtain the target labeled sample, add the target labeled sample to the training sample set to obtain the updated training sample set; the updated training sample set is used to train the target neuron The network goes through network training again.
  • FIG. 2 shows a schematic diagram of a system architecture that can be used in a method for generating a training sample set in an embodiment of the present disclosure
  • the system architecture includes: an acquisition terminal 201, a network 202 And control terminal 203.
  • the acquisition terminal 201 and the control terminal 203 establish a communication connection through the network 202, the acquisition terminal 201 reports the obtained unlabeled samples and the target neural network to the control terminal 203 through the network 202, and the control terminal 203 acquires After arriving at each unlabeled sample and target neural network, determine the estimated influence degree value of each unlabeled sample on the network training of the target neural network; and select the target whose estimated influence degree value meets the preset requirements from each unlabeled sample Unlabeled samples; when the selected target unlabeled samples are labeled to obtain the target labeled samples, the target labeled samples are added to the training sample set to obtain an updated training sample set; the updated training sample set is used for The target neural network undergoes network training again. Finally, the control terminal 203 sends the updated training sample set to the acquisition terminal 201 .
  • the acquisition terminal 201 may be an image acquisition device, and the control terminal 203 may include a data processing device with data processing capability or a remote server.
  • the network 202 may be connected in a wired or wireless manner.
  • the control terminal 203 is a data processing device
  • the acquisition terminal 201 can communicate with the data processing device through a wired connection, such as performing data communication through a bus; Data exchange between the network and the remote server.
  • the application scenario of the method will be described in detail below.
  • the method for generating the training sample set in the embodiments of the present disclosure can be applied to the training preparation process of the neural network in any application scenario.
  • the training sample set can be a set of marked samples. How to automatically select the target unlabeled samples that can adapt to the target neural network training from a large number of unlabeled samples has become a key task to update the training sample set.
  • the manual screening method provided in related technologies results in high manpower and material costs, while the screening method related to active learning either screens unlabeled samples that are not accurate enough, or takes a long time to achieve screening.
  • the embodiment of the present disclosure provides an unlabeled sample screening scheme based on estimated influence degree value estimation, so that the target neural network trained by the obtained updated training sample set is more accurate, and Automated screening, saving time and effort.
  • the target neural network here is also different.
  • the target neural network here can be a classification network that determines the target classification; for another example, in the target detection application scenario, the target neural network here can determine the target position, size and other information detection network.
  • the target neural network here may also be other networks, which is not limited in this embodiment of the present disclosure.
  • the target neural network here can be trained based on a training sample set containing several labeled samples, for example, it can be a vehicle classification network trained based on multiple labeled vehicle pictures.
  • the estimated influence degree value of each unlabeled sample on the network training of the target neural network can be determined here.
  • the estimated influence degree value of each unlabeled sample can be determined, and the influence of each unlabeled sample on the estimated influence degree value of the network training of the target neural network can be converted into each first marked The estimated influence degree and value of the sample on the network training parameters, and the estimated influence value of each unlabeled sample on the network training parameters. This is considering that network training parameters are the most direct considerations for network training, and the degree of proximity between the estimated influence degree and value of each first labeled sample and the estimated influence degree value of each unlabeled sample can be determined to a certain extent. It reflects the role that unlabeled samples can play in the training of the target neural network.
  • the target whose estimated influence degree value meets the preset requirements can be selected from each unlabeled sample Unlabeled samples.
  • unlabeled samples with estimated influence degree values greater than the second preset threshold may be selected as target unlabeled samples; or the unlabeled samples may be sorted in order of estimated influence degree values from large to small, Then determine the target unlabeled samples according to the ranking results; for example, select the unlabeled samples whose ranking results are in the top 10 as the target unlabeled samples.
  • the second preset threshold is set according to the type of the selected target neural network. Since the selected target neural network type to be trained is different, the estimated influence value of each unlabeled sample is also different. Therefore, the second preset The setting of the threshold is also different.
  • the target unlabeled sample When the target unlabeled sample is selected, the target unlabeled sample can be firstly labeled, and then the labeled target labeled sample can be added to the training sample set to obtain an updated training sample set.
  • each round of update can be achieved through the following steps:
  • Step 1 Screen out the target unlabeled samples from each unlabeled sample to obtain each updated unlabeled sample; and determine the updated target neural network based on the training of the updated training sample set; the updated training sample set includes each the first labeled sample and the target labeled sample;
  • Step 2 Based on the updated unlabeled samples and the updated target neural network, determine the estimated influence degree value of the updated unlabeled samples on the network training of the updated target neural network;
  • Step 3 Select the target unlabeled samples whose estimated impact degree value meets the preset requirements from the updated unlabeled samples;
  • Step 4 In the case of labeling the selected target unlabeled samples to obtain the target labeled samples, add the target labeled samples to the updated training sample set, and obtain the updated target neural network for training. training sample set.
  • the target non-concerned samples that meet the preset requirements are unlabeled samples whose estimated influence degree value is greater than the second preset threshold value as the target unlabeled sample;
  • the small order sorts each unlabeled sample, and then determines the target unlabeled sample according to the sorting result.
  • the target unlabeled samples can be screened out from each unlabeled sample to obtain the updated unlabeled samples, and at this time, the target labeled samples corresponding to the target unlabeled samples can be added to the updated training sample set, And the updated target neural network can be obtained by training based on the updated training sample set.
  • the estimated influence degree value of each updated unlabeled sample can be determined based on each updated unlabeled sample and the updated target neural network, and then the target unlabeled sample is selected and the updated training sample set is updated again. , and so on, until the loop cut-off condition is reached, and the updated target neural network is obtained.
  • the above-mentioned cycle cut-off condition may be that the number of cycles reaches the preset number of times, or that the relevant evaluation indicators of the updated target neural network obtained after training reach the preset indicators, for example, the cycle cut-off condition that the prediction accuracy reaches 0.75 .
  • determining the estimated impact degree value may include the following steps:
  • Step 1 Based on each first labeled sample and the target neural network, determine the estimated influence degree of each first labeled sample on the network training parameters during the forward propagation process of the target neural network based on each first labeled sample and values; and, based on each unlabeled sample and the target neural network, determine the estimated influence degree value of each unlabeled sample on the network training parameters during the forward propagation of the target neural network based on each unlabeled sample;
  • Step 2 Based on the estimated influence degree and value of each first labeled sample on the network training parameters and the estimated influence degree value of each unlabeled sample on the network training parameters, determine the network effect of each unlabeled sample on the target neural network. The estimated impact value for training.
  • the determination of the estimated influence degree and value of each first labeled sample on the network training parameters can be realized based on the forward propagation of the labeled samples; secondly, each unlabeled sample can be realized based on the forward propagation of the unlabeled samples Determination of the estimated influence degree value of the sample on the network training parameters; finally, the estimated influence degree value of each unlabeled sample on the network training of the target neural network can be determined based on the above estimated influence degree sum value and the estimated influence degree value .
  • Forward propagation in the embodiments of the present disclosure may refer to a process of inputting samples into a trained target neural network to obtain a gradient and a Hessian matrix corresponding to a relevant loss function. During the forward propagation, the network parameter values of the target neural network are not adjusted.
  • the determination of the estimated influence degree of unlabeled samples on network training parameters can be realized based on the determination of pseudo-labeled information, which can be achieved by the following steps:
  • Step 1 For each unlabeled sample in each unlabeled sample, input each unlabeled sample into the target neural network, and determine the probability value for each candidate prediction result output by the target neural network;
  • Step 2 Based on the probability values for each candidate prediction result, determine the pseudo-label information of the unlabeled sample.
  • Step 3 Based on the pseudo-label information, determine the gradient value corresponding to the loss function of the target neural network in the case of forward propagation of unlabeled samples;
  • Step 4 The determined gradient value is used as the estimated influence degree value of the unlabeled samples on the network training parameters.
  • the probability values output by the target neural network for each candidate prediction result can be determined, and then the pseudo-label information of the unlabeled sample can be determined based on the probability values for each candidate prediction result .
  • the strategies for generating pseudo-label information for different target neural networks are also different.
  • the target neural network is a classification network and the candidate prediction result is a candidate category
  • the candidate category with the highest probability value can be determined as the pseudo-label information of the unlabeled sample
  • the prediction result is a candidate detection frame
  • the candidate detection frame whose probability value is greater than the first preset threshold is determined as the pseudo-label information of the unlabeled sample, that is, multiple candidate detection frames can be used as the pseudo-label information.
  • the first preset threshold can be set to 0.95, that is, the candidate detection frame with a probability value greater than 0.95 is determined as the pseudo-labeled information of the unlabeled sample.
  • the corresponding pseudo-label information generation strategy can also be determined for other target neural networks.
  • the loss of unlabeled samples can be determined through pseudo-label information, and the gradient value corresponding to the loss function of the target neural network can be determined by backpropagating the loss to the target neural network, which can be used as the estimated influence degree of unlabeled samples on network training parameters value.
  • the first marked sample is input into the target neural network, and the gradient value corresponding to the loss function of the target neural network and the Hessian matrix corresponding to the loss function of the target neural network are obtained.
  • the gradient value here is used to represent the degree of influence of each network parameter on the loss function in the case of forward propagation of the first labeled sample
  • the Hessian matrix here is used to represent the forward propagation of the first labeled sample.
  • the degree to which each network parameter affects the loss function is affected by other network parameters.
  • the above gradient value corresponds to the first derivative of the loss function
  • the Hessian matrix corresponds to the second derivative of the loss function.
  • the gradient value corresponding to the loss function of the target neural network obtained by each labeled sample and the Hessian matrix corresponding to the loss function of the target neural network can be superimposed to obtain the gradient sum value and Hessian The matrix and value, and then based on the product operation of the gradient sum value and the Hessian matrix sum value, determine the estimated influence degree and value.
  • the network performance test function of the training reference set can be combined to realize the determination of the estimated influence degree and value, and then determine the estimated influence degree value of each unlabeled sample on the network training of the target neural network, which can be obtained by follows these steps to achieve:
  • Step 1 Obtain each second labeled sample included in the training reference set; the training reference set and the training sample set do not have the same labeled samples;
  • Step 2 Based on each of the first labeled samples, each of the second labeled samples, and the target neural network, determine that in the process of forward propagation of the target neural network based on each of the first labeled samples and each of the second labeled samples, each The estimated influence degree and value of the first labeled sample and each second labeled sample on the network training parameters; and, based on each unlabeled sample and the target neural network, determine the forward propagation of the target neural network based on each unlabeled sample In the process of , each unlabeled sample has an estimated influence value on the network training parameters;
  • Step 3 Based on the estimated influence degree and value of each first labeled sample and each second labeled sample on the network training parameters and the estimated influence degree value of each unlabeled sample on the network training parameters, determine each unlabeled sample The value of the estimated influence of the sample on the network training of the target neural network.
  • multiple first labeled samples may be selected from each first labeled sample, and each first labeled sample in the multiple first labeled samples
  • the marked samples are input into the target neural network, and the Hessian matrix and the value corresponding to the loss function of the target neural network are obtained;
  • each second marked sample in each second marked sample can be input into the target neural network, Obtain the gradient and value corresponding to the loss function of the target neural network; finally, based on the product operation of the gradient sum value and the Hessian matrix sum value, determine the estimated influence degree and value.
  • the first labeled sample for training the target neural network and the second labeled sample for performance testing of the target neural network are distinguished, so that a more accurate network can be determined while valid network testing Estimated magnitude and value of impact.
  • the present disclosure can be based on the gradient and value of the target neural network on the training reference set R inverse matrix with Hessian and values and the expected gradient value of the unlabeled sample z i to determine the impact of unlabeled samples on model performance, that is, to determine To measure the importance of sample zi , where the gradient and value can correspond to the degree of influence of each network parameter on the loss function in the case of forward propagation of each second labeled sample in each second labeled sample , the Hessian matrix and the value can correspond to the degree of influence of each network parameter on the loss function by the degree of influence of other network parameters in the case of forward propagation of each first labeled sample in each first labeled sample .
  • the Hessian matrix and the values can be defined as the sum of the Hessian matrices of all the first labeled samples, namely
  • the Hessian matrix and value will not be directly calculated, but the calculation and The product of , here the product can be recorded as s test , where s test can be determined by random estimation.
  • the gradient and value of the network on the reference set can be first Denote it as v, and then randomly select k samples ⁇ z 1 ,z 2 ,...,z k ⁇ from the first labeled sample, and initialize In the case of iterating through Second, the obtained s test results can be determined as the estimated impact degree and value.
  • the process of determining the degree and value of the estimated impact it can be realized through multiple rounds of iterative calculations.
  • the marked samples pointed to by the current round of iterative calculations are determined, based on the determined marked samples corresponding to Hessian matrix, gradient and value, and the estimated influence degree and value corresponding to the previous round of iterative operation, determine the estimated influence degree and value corresponding to the current round of iterative operation, and through multiple rounds of iterations, the final estimated influence can be obtained degree and value.
  • the expected gradient determined here Slightly different.
  • the unlabeled picture z i is forward propagated to the network, the category with the highest predicted score of the classifier is selected as the pseudo-labeling result p, and the pseudo-labeling result is used to determine the loss of the picture in turn will lose Backpropagation to the neural network to get the gradient by as the expected gradient for unlabeled samples
  • the unlabeled picture z i is propagated forward to the network, and all detection frames P' of the picture z i by the network are obtained.
  • a threshold such as 0.95
  • I(z i ,R) the more negative the value of I(z i ,R), the more positive impact the sample z i can have on network performance.
  • the N samples with the most negative values are selected for labeling and added to the first labeled sample to obtain an updated training sample set.
  • the writing order of each step does not mean a strict execution order and constitutes any limitation on the implementation process.
  • the specific execution order of each step should be based on its function and possible
  • the inner logic is OK.
  • the embodiment of the present disclosure also provides a device for generating a training sample set corresponding to the method for generating a training sample set. Since the problem-solving principle of the device in the embodiment of the present disclosure is the same as that of the above-mentioned training sample set in the embodiment of the present disclosure The generated method is similar, so the implementation of the device can refer to the implementation of the method.
  • FIG. 3 it is a schematic structural diagram of a device for generating a training sample set provided by an embodiment of the present disclosure.
  • the device includes: an acquisition module 301, a determination module 302, a selection module 303, and a generation module 304; wherein,
  • the obtaining module 301 is configured to obtain each unlabeled sample and the target neural network trained based on the training sample set;
  • the determination module 302 is configured to determine the estimated influence degree value of each unlabeled sample on the network training of the target neural network based on each unlabeled sample and the target neural network;
  • the selection module 303 is configured to select target unlabeled samples whose estimated influence degree value meets the preset requirements from each unlabeled sample;
  • the generating module 304 is configured to add the target marked sample to the training sample set to obtain the updated training sample set when the target unlabeled sample is sample marked to obtain the target marked sample, and the updated training sample set is used In order to perform network training on the target neural network again.
  • the target labeled samples can be obtained to update the training sample set.
  • the present disclosure realizes the automatic selection of unlabeled samples based on the estimated influence degree value, which is more time-saving and labor-saving than the manual selection scheme, and also reduces the subsequent labeling cost.
  • the training sample set includes each first labeled sample; the determining module 302 is configured to determine the contribution of each unlabeled sample to the target neural network based on the following steps: Estimated impact value for network training:
  • each first marked sample and the target neural network Based on each first marked sample and the target neural network, determine the estimated influence degree and value of each first marked sample on the network training parameters during the forward propagation of the target neural network based on each first marked sample; as well as,
  • the determination module 302 is configured to determine the estimated impact degree and value according to the following steps:
  • the gradient sum value is used to represent The summation result of the gradient value corresponding to each first labeled sample, the gradient value is used to represent the degree of influence of each network parameter on the loss function in the case of forward propagation of the first labeled sample;
  • the Hessian matrix sum value is used In order to represent the summation result of the Hessian matrix corresponding to each first labeled sample, the Hessian matrix is used to indicate that in the case of forward propagation of the first labeled sample, the degree of influence of each network parameter on the loss function is affected by other The degree of influence of network parameters;
  • the estimated influence degree and value are determined.
  • the determination module 302 is configured to determine the estimated influence degree value of each unlabeled sample on the network training of the target neural network based on each unlabeled sample and the target neural network according to the following steps:
  • each first labeled sample, each second labeled sample, and the target neural network Based on each first labeled sample, each second labeled sample, and the target neural network, it is determined that each first labeled sample and each second labeled sample are used for forward propagation of the target neural network.
  • the determination module 302 is configured to determine the estimated impact degree and value according to the following steps:
  • the estimated influence degree and value are determined.
  • the determination module 302 is configured to determine the estimated influence degree and value based on the product operation of the gradient sum value and the Hessian matrix sum value according to the following steps:
  • For the current round of iterative operations determine the marked samples pointed to by the current round of iterative operations, and determine the current The estimated influence degree and value corresponding to the round iterative operation.
  • the determination module 302 is configured to determine, based on each unlabeled sample and the target neural network, in the process of forward propagation of the target neural network based on each unlabeled sample, each unlabeled
  • the determined gradient value is used as the estimated influence degree value of the unlabeled sample on the network training parameters.
  • the determining module 302 is configured to determine the pseudo-labeling information of the unlabeled samples based on the probability values for each candidate prediction result according to the following steps:
  • the candidate prediction result is a candidate category
  • the candidate detection frame whose probability value is greater than the first preset threshold is determined as the pseudo-labeled information of the unlabeled sample.
  • the selection module 303 is configured to select target unlabeled samples whose estimated influence degree value meets the preset requirements from each unlabeled sample according to the following steps:
  • the generating module 304 is further configured to:
  • the updated training sample set includes each first already Labeled samples and target labeled samples
  • FIG. 4 is a schematic structural diagram of the electronic device provided by the embodiment of the present disclosure, including: a processor 401 , a memory 402 , and a bus 403 .
  • the memory 402 stores machine-readable instructions executable by the processor 401 (for example, the execution instructions corresponding to the acquisition module 301, the determination module 302, the selection module 303, and the generation module 304 in the device in FIG. , the processor 401 communicates with the memory 402 through the bus 403, and when the machine-readable instructions are executed by the processor 401, the following processing is performed:
  • the target labeled samples are added to the training sample set to obtain the updated training sample set; the updated training sample set is used to retrain the target neural network Do network training.
  • An embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored.
  • the computer program is run by a processor, the method for generating the training sample set described in the above-mentioned method embodiment is executed. step.
  • the storage medium may be a volatile or non-volatile computer-readable storage medium.
  • An embodiment of the present disclosure also provides a computer program.
  • the computer program includes computer readable codes.
  • the processor of the electronic device executes the training samples as described in any of the above embodiments. The steps of the method for set generation.
  • Embodiments of the present disclosure also provide a computer program product, the computer program product carries program code, and the instructions included in the program code can be configured to execute the steps of the method for generating the training sample set described in the method embodiment above, which can be See the method example above.
  • the above-mentioned computer program product may be realized by hardware, software or a combination thereof.
  • the computer program product may be embodied as a computer storage medium, and in other embodiments, the computer program product may be embodied as a software product, such as a software development kit (Software Development Kit, SDK) and the like.
  • the device involved in the embodiments of the present disclosure may be at least one of a system, a method, and a computer program product.
  • a computer program product may include a computer readable storage medium having computer readable program instructions thereon for causing a processor to implement various aspects of the present disclosure.
  • a computer readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device.
  • a computer readable storage medium may be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Examples of computer-readable storage media include: portable computer disks, hard disks, Random Access Memory (RAM), Read-Only Memory (ROM), erasable Electrical Programmable Read Only Memory (EPROM) or flash memory, Static Random-Access Memory (Static Random-Access Memory, SRAM), Portable Compact Disc Read-Only Memory (CD-ROM), Digital Video Discs (DVDs), memory sticks, floppy disks, mechanically encoded devices such as punched cards or raised structures in grooves with instructions stored thereon, and any suitable combination of the foregoing.
  • RAM Random Access Memory
  • ROM Read-Only Memory
  • EPROM erasable Electrical Programmable Read Only Memory
  • flash memory Static Random-Access Memory
  • SRAM Static Random-Access Memory
  • CD-ROM Portable Compact Disc Read-Only Memory
  • DVDs Digital Video Discs
  • memory sticks floppy disks, mechanically encoded devices such as punched cards or raised structures in grooves with instructions stored thereon, and any suitable combination of the foregoing.
  • computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., pulses of light through fiber optic cables), or transmitted electrical signals.
  • the computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device over at least one of a network, such as the Internet, a local area network, a wide area network, and a wireless network.
  • the network may include at least one of copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and edge servers.
  • a network adapter card or a network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .
  • Computer program instructions for performing the operations of the present disclosure may be assembly instructions, Industry Standard Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or in one or more source or object code written in any combination of programming languages, including object-oriented programming languages—such as Smalltalk, C++, etc., and conventional procedural programming languages, such as the “C” language or similar programming languages.
  • Computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server implement.
  • the remote computer may 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 may be connected to an external computer (for example, using Internet Service Provider to connect via the Internet).
  • LAN Local Area Network
  • WAN Wide Area Network
  • electronic circuits such as programmable logic circuits, FPGAs, or programmable logic arrays (Programmable Logic Arrays, PLAs), can be customized by using state information of computer-readable program instructions, which can execute computer-readable Read program instructions, thereby implementing various aspects of the present disclosure.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.
  • the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor.
  • the computer software product is stored in a storage medium, including several
  • the instructions are used to make an electronic device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present disclosure.
  • the aforementioned storage medium includes: various media capable of storing program codes such as U disk, mobile hard disk, ROM, random access memory RAM, magnetic disk or optical disk.
  • the present disclosure provides a method, device, electronic device, storage medium, and program for generating a training sample set, wherein the method includes: obtaining each unlabeled sample and the target neural network trained based on the training sample set; Label the samples and the target neural network, and determine the estimated influence degree value of each unlabeled sample on the network training of the target neural network; select the target unlabeled sample whose estimated influence degree value meets the preset requirements from each unlabeled sample; When the selected target unlabeled sample is sample-labeled to obtain the target labeled sample, the target labeled sample is added to the training sample set to obtain the updated training sample set; the updated training sample set is used to re-train the target neural network. network training.

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Abstract

La présente divulgation concerne un procédé et un appareil pour générer un ensemble d'échantillons d'apprentissage, et un dispositif électronique, un support de stockage et un programme. Le procédé comprend les étapes consistant à : acquérir chaque échantillon non étiqueté, et un réseau neuronal cible obtenu par la mise en oeuvre d'un apprentissage sur la base d'un ensemble d'échantillons d'apprentissage ; sur la base de chaque échantillon non étiqueté et du réseau neuronal cible, déterminer une valeur de degré d'effet estimé de chaque échantillon non étiqueté relative à l'apprentissage réseau du réseau neuronal cible ; sélectionner, parmi des échantillons non étiquetés, un échantillon non étiqueté cible présentant la valeur de degré d'effet estimé satisfaisant une exigence prédéfinie ; et lorsqu'un échantillon étiqueté cible est obtenu par la mise en oeuvre d'un étiquetage d'échantillon sur l'échantillon non étiqueté cible sélectionné, ajouter l'échantillon étiqueté cible à l'ensemble d'échantillons d'apprentissage afin d'obtenir un ensemble d'échantillons d'apprentissage mis à jour, l'ensemble d'échantillons d'apprentissage mis à jour étant utilisé pour mettre en oeuvre un nouvel apprentissage réseau sur le réseau neuronal cible. La présente divulgation permet de réaliser une sélection automatique d'un échantillon non étiqueté sur la base d'une valeur de degré d'effet estimé, qui, par rapport à un système de sélection manuelle, permet des économies de temps et de travail, et réduit également des coûts d'étiquetage ultérieur.
PCT/CN2022/078350 2021-08-19 2022-02-28 Procédé et appareil pour générer un ensemble d'échantillons d'apprentissage, et dispositif électronique, support de stockage et programme WO2023019908A1 (fr)

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