CN107871142A - A kind of empty convolution method based on depth convolution confrontation network model - Google Patents

A kind of empty convolution method based on depth convolution confrontation network model Download PDF

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CN107871142A
CN107871142A CN201711123735.3A CN201711123735A CN107871142A CN 107871142 A CN107871142 A CN 107871142A CN 201711123735 A CN201711123735 A CN 201711123735A CN 107871142 A CN107871142 A CN 107871142A
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convolution
mrow
empty
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周智恒
李立军
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South China University of Technology SCUT
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Abstract

The invention discloses a kind of empty convolution method based on depth convolution confrontation network model, belong to deep learning field of neural networks, described empty convolution method comprises the following steps：S1, construction are originally generated confrontation network model；S2, construction depth convolutional neural networks are as maker and arbiter；S3, initialization random noise, are inputted in maker；S4, convolution operation is carried out to image using empty convolution in neutral net；S5, loss function that empty convolution operation obtains input maker subsequently trained.The empty convolution method based on depth convolution confrontation network model of this method structure, change arbiter, maker receives the convolution mode after picture, arbiter, maker can be learnt with bigger scope to the feature of image, so as to improve the robustness of whole network training pattern.

Description

A kind of empty convolution method based on depth convolution confrontation network model
Technical field
The present invention relates to deep learning nerual network technique field, and in particular to one kind is based on depth convolution confrontation network mould The empty convolution method of type.
Background technology
Production confrontation network (Generative Adversarial Network, abbreviation GAN) is by Goodfellow In the deep learning framework that 2014 propose, it is based on the thought of " game theory ", construction maker (generator) and arbiter (discriminator) two kinds of models, the former generates image by the Uniform noise or gaussian random noise for inputting (0,1), after Person differentiates to the image of input, it is determined that being the image from data set or the image as caused by maker.
In traditional confrontation network model, operation that arbiter is carried out often by original convolution kernel, to generation The feature of device generation image is parsed, and carries out a kind of study of new feature, generally requires to arrive a kind of old feature knot in study It could be carried out after beam.In this case, the characteristic range that maker learns is smaller, and learning efficiency is relatively low.
The content of the invention
The invention aims to solve drawbacks described above of the prior art, there is provided one kind is based on depth convolution confrontation net The empty convolution method of network model.
The purpose of the present invention can be reached by adopting the following technical scheme that：
A kind of empty convolution method described in empty convolution method based on depth convolution confrontation network model includes following Step：
S1, construction are originally generated confrontation network model, and generating image by maker inputs to arbiter progress network instruction Practice；
S2, construction depth convolutional neural networks are as maker and arbiter；
S3, initialization random noise, are inputted in maker；
S4, convolution operation is carried out to image using empty convolution in neutral net；
S5, loss function that empty convolution operation obtains input maker subsequently trained.
Further, described step S4 detailed processes are as follows：
S41, the multiple different numerical value of construction but size identical convolution kernel；
S42, using empty convolution convolution kernel is transformed, input network is trained.
Further, described step S5 detailed processes are as follows：
S51, the characteristics of image figure that will be obtained after empty convolution, input in arbiter and are differentiated；
S52, loss function that empty convolution operation obtains input maker subsequently trained.
Further, the expression formula of described loss function is：
Wherein, D (x) represents differentiation of the arbiter to image, and pr represents the distribution of data images, and pg represents generation image Distribution, λ is hyper parameter,For gradient, E is the functional symbol for taking average.
The present invention is had the following advantages relative to prior art and effect：
Robustness：The present invention sets according to the operating process of empty convolution and constructs multiple empty convolution kernels, pass through convolution Core inserts the mode of " 0 ", applies in the confrontation network model of maker and arbiter is served as with depth convolutional neural networks, more It hurry up, more stably carry out network training.
Brief description of the drawings
Fig. 1 is the overall flow figure disclosed by the invention for being originally generated confrontation network and being trained by empty convolution；
Fig. 2 is the schematic diagram for being transformed into empty convolution kernel in the present invention to original convolution core.
Embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is Part of the embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art The every other embodiment obtained under the premise of creative work is not made, belongs to the scope of protection of the invention.
Embodiment
Present embodiment discloses a kind of empty convolution method based on depth convolution confrontation network model, specifically include following Step：
Step S1, construction is originally generated confrontation network model, is inputted by maker generation image to arbiter and carries out net Network training.
Step S2, construction depth convolutional neural networks are as maker and arbiter；
Different convolution kernels, it is embodied in difference, the difference of ranks number of matrix numerical value.
Multiple convolution kernels are constructed, during image is handled, different convolution kernels is meant in network training Different characteristic of the process learning to generation image.
In the model of tradition confrontation network, the convolution kernel used in arbiter and maker is all fixed size and numerical value Consistent, training effectiveness in this case is relatively low, and the characteristics of image scope learnt is relatively small.And at this In invention, using empty convolution, the operation of " 0 " is interleave in being carried out to original convolution core, can be learnt so as to increase convolution kernel The characteristic range arrived, further increase the efficiency of whole network study.
In actual applications, it should which according to the complexity of data images feature, the number of convolution kernel is set.
Step S3, random noise is initialized, is inputted in maker.
Step S4, convolution operation is carried out to image using empty convolution in neutral net.
Detailed process is as follows：
S41, the multiple different numerical value of construction but size identical convolution kernel；
S42, using the convolution kernel constructed, convolution is carried out to multiple images of maker generation respectively, it is more so as to obtain Open characteristic pattern.
Step S5, the loss function input maker that empty convolution operation obtains subsequently is trained.Detailed process is such as Under：
S51, by the characteristic pattern after convolution in step S4, input arbiter is differentiated；
S52, loss function that empty convolution operation obtains input maker subsequently trained；
S53, the average of all loss functions is inputted and continues to be trained into maker.
The effect of loss function is to weigh the ability that arbiter is judged generation image.The value of loss function is smaller, explanation In current iteration, arbiter can have the generation image of preferable performance discrimination maker；Property that is on the contrary then illustrating arbiter Can be poor.
The expression formula of loss function is：
Wherein, D (x) represents differentiation of the arbiter to image, and pr represents the distribution of data images, and pg represents generation image Distribution, λ is hyper parameter,For gradient, E is the functional symbol for taking average.
In summary, present embodiment discloses a kind of empty convolution method based on depth convolution confrontation network model, phase Than in traditional original confrontation network model, changing arbiter and receiving the mode learnt to characteristics of image after picture. In the model of tradition confrontation network, arbiter is all fixed size with the convolution kernel used in maker and numerical value is consistent, Training effectiveness in this case is relatively low, and the characteristics of image scope learnt is relatively small.And in the present invention, Using empty convolution, the operation of " 0 " is interleave in being carried out to original convolution core, so as to increase the feature that convolution kernel can learn Scope, further increase the efficiency of whole network study.
Above-described embodiment is the preferable embodiment of the present invention, but embodiments of the present invention are not by above-described embodiment Limitation, other any Spirit Essences without departing from the present invention with made under principle change, modification, replacement, combine, simplification, Equivalent substitute mode is should be, is included within protection scope of the present invention.

Claims (4)

  1. A kind of 1. empty convolution method based on depth convolution confrontation network model, it is characterised in that described empty convolution side Method comprises the following steps：
    S1, construction are originally generated confrontation network model, and generating image by maker inputs to arbiter progress network training；
    S2, construction depth convolutional neural networks are as maker and arbiter；
    S3, initialization random noise, are inputted in maker；
    S4, convolution operation is carried out to image using empty convolution in neutral net；
    S5, loss function that empty convolution operation obtains input maker subsequently trained.
  2. 2. a kind of empty convolution method based on depth convolution confrontation network model according to claim 1, its feature exist In described step S4 detailed processes are as follows：
    S41, the multiple different numerical value of construction but size identical convolution kernel；
    S42, using empty convolution convolution kernel is transformed, input network is trained.
  3. A kind of 3. depth convolution confrontation network model based on empty convolution according to claim 1, it is characterised in that institute The step S5 detailed processes stated are as follows：
    S51, the characteristics of image figure that will be obtained after empty convolution, input in arbiter and are differentiated；
    S52, loss function that empty convolution operation obtains input maker subsequently trained.
  4. A kind of 4. depth convolution confrontation network model based on empty convolution according to claim 3, it is characterised in that institute The expression formula for the loss function stated is：
    <mrow> <mi>L</mi> <mrow> <mo>(</mo> <mi>D</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mrow> <mo>-</mo> <mi>E</mi> </mrow> <mrow> <mi>x</mi> <mo>~</mo> <mi>pr</mi> </mrow> </msub> <mo>[</mo> <mi>D</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>]</mo> <mo>+</mo> <msub> <mi>E</mi> <mrow> <mi>x</mi> <mo>~</mo> <mi>pg</mi> </mrow> </msub> <mo>[</mo> <mi>D</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>]</mo> <mo>+</mo> <msub> <mi>&amp;lambda;E</mi> <mrow> <mi>x</mi> <mo>~</mo> <mi>X</mi> </mrow> </msub> <msub> <mo>&amp;dtri;</mo> <mi>x</mi> </msub> </mrow>
    Wherein, D (x) represents differentiation of the arbiter to image, and pr represents the distribution of data images, and pg represents point of generation image Cloth, λ are hyper parameter,For gradient, E is the functional symbol for taking average.
CN201711123735.3A 2017-11-14 2017-11-14 A kind of empty convolution method based on depth convolution confrontation network model Pending CN107871142A (en)

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CN110363210A (en) * 2018-04-10 2019-10-22 腾讯科技（深圳）有限公司 A kind of training method and server of image, semantic parted pattern
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CN109214406A (en) * 2018-05-16 2019-01-15 长沙理工大学 Based on D-MobileNet neural network image classification method
CN110610184A (en) * 2018-06-15 2019-12-24 阿里巴巴集团控股有限公司 Method, device and equipment for detecting salient object of image
CN110610184B (en) * 2018-06-15 2023-05-12 阿里巴巴集团控股有限公司 Method, device and equipment for detecting salient targets of images
CN109345441A (en) * 2018-10-19 2019-02-15 上海唯识律简信息科技有限公司 A kind of de-watermarked method and system of image based on generation confrontation network
CN109615059A (en) * 2018-11-06 2019-04-12 海南大学 Edge filling and filter dilation operation method and system in a kind of convolutional neural networks
CN109523538A (en) * 2018-11-21 2019-03-26 上海七牛信息技术有限公司 A kind of people counting method and system based on generation confrontation neural network
CN109784149B (en) * 2018-12-06 2021-08-20 苏州飞搜科技有限公司 Method and system for detecting key points of human skeleton
CN109741328A (en) * 2019-02-02 2019-05-10 东北大学 A kind of automobile apparent mass detection method based on production confrontation network
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CN110136731A (en) * 2019-05-13 2019-08-16 天津大学 Empty cause and effect convolution generates the confrontation blind Enhancement Method of network end-to-end bone conduction voice

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Application publication date: 20180403