CN110097103A - Based on the semi-supervision image classification method for generating confrontation network - Google Patents

Based on the semi-supervision image classification method for generating confrontation network Download PDF

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CN110097103A
CN110097103A CN201910323702.6A CN201910323702A CN110097103A CN 110097103 A CN110097103 A CN 110097103A CN 201910323702 A CN201910323702 A CN 201910323702A CN 110097103 A CN110097103 A CN 110097103A
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network
training
sample
classification
arbiter
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田小林
李帅
李芳�
荀亮
李娇娇
焦李成
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Xidian University
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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
    • G06F18/2155—Generating training patterns; Bootstrap methods, e.g. bagging or boosting characterised by the incorporation of unlabelled data, e.g. multiple instance learning [MIL], semi-supervised techniques using expectation-maximisation [EM] or naïve labelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques

Abstract

The invention discloses a kind of based on the semi-supervision image classification method for generating confrontation network, mainly solve the problems, such as that existing unsupervised learning nicety of grading is low and semi-supervised learning needs a large amount of accurate labels, implementation step are as follows: 1) choose and download standard picture training sample and test sample；2) setting network supervised learning relevant parameter, and build one and network is fought by the generation that generator network, arbiter network and subsidiary classification device form side by side；3) it is trained using stochastic gradient descent method to confrontation network is generated；4) test sample to be sorted is input in trained generation confrontation network model, exports the classification of image to be detected.The present invention improves the image classification accuracy of unsupervised learning, and can obtain good image classification effect on the sample set for containing only a small amount of accurate mark sample, can be used for target classification in actual scene.

Description

Based on the semi-supervision image classification method for generating confrontation network
Technical field
The invention belongs to technical field of image processing, further relate to a kind of semi-supervision image classification method, can be used for Target classification in actual scene.
Background technique
The main task of image classification can be achieved on the detection that the target in the image to input carries out classification, Jin Erzhun Really set the goal belonging classification really.As people are to the understanding that deepens continuously of computer vision field, image classification is at this Field is used widely and is developed, and currently exists a large amount of sorting algorithm to realize image classification.For tape label Image classification task based on supervised learning has developed more mature, and preferable classification can be obtained on standard data set Precision, but in actual application scenarios, the training dataset of network is not easy to obtain, such as in terms of medical diagnosis, number According to the acquisition of sample with regard to relatively difficult, the expert that the image that secondary acquisition precisely marks requires experience is labeled, this Sample can expend huge time cost and human cost.Supervised learning itself needs largely to have the training number accurately marked According to the quality and quantity of training data will have a direct impact on the nicety of grading of image classification task, this results in being difficult to obtain Supervised learning can not obtain preferable completion classification task in the application scenarios of training sample, and the training sample marked on a small quantity can be led Cause over-fitting.And semi-supervised learning is suitable for few in labeled data, and uses under the scene more than training data, has in use It can get preferable training effect on the basis of limitation labeled data, reduce the degree of dependence to labeled data, greatly enrich The application scenarios of classification task.
IanJ.Goodfellow, JeanPouget-Abadie, MehdiMirza, BingXu, David Ware- The paper that Farley, SherjilOzair, AaronCourville, YoshuaBengio are delivered at it Disclosed in " GenerativeAdversarialNets " it is a kind of utilize convolutional neural networks extract characteristics of image it is two stage Image generates and classification method.This method respectively generates network and differentiates network first by establishing two network structures, Middle generation network is responsible for receiving a noise vector, maps it onto a false sample image, by this false sample image It is input in arbiter network together with authentic specimen image, by differentiating that network carries out the true and false to authentic specimen and false sample Determine, characteristic pattern is classified using class probability.Although this method realizes the classification to natural image, still, the party The shortcoming that method still has is to mention the characteristics of image of true picture and false image in the last layer neural network It takes, there is no sufficiently accurate trained label due to only carrying out unsupervised learning sample set, lead to not be exactly found classification boundary, So that nicety of grading is not ideal enough, it is not able to satisfy the requirement of realistic objective classification.
Patent document " Classification of Polarimetric SAR Image based on generation confrontation network of the Xian Electronics Science and Technology University in its application Method " discloses in (applying date: on 08 25th, 2017, application number: 201710742716.2, publication number: 107563428A) A kind of Classification of Polarimetric SAR Image method based on generation confrontation network.This method uses polarimetric synthetic aperture radar to be sorted The coherence matrix of SAR image uses filter window size for 7 × 7 Lee's Lee filter, carries out to coherence matrix as input Filtering, the coherence matrix after being denoised extract 9 dimensional features as a sample from the coherence matrix after denoising, will denoise All samples in coherence matrix afterwards generate a sample set, and 10% sample is randomly selected from the sample set of each ground species This conduct has exemplar collection, gives birth to 90% sample remaining in the sample set of each ground species as unlabeled exemplars collection training At network and confrontation network.But there is still a need for 10% marker samples to divide as training sample set image for this method Class training, although being suitable for the classification of SAR image, to medical image, natural image is sufficient amount of in the presence of being difficult to obtain Accurate mark training sample problem, greatly limits its application range.
Summary of the invention
It is a kind of based on half prison for generating confrontation network it is an object of the invention in view of the above shortcomings of the prior art, propose Image classification method is superintended and directed, increases a subsidiary classification device network on arbiter network to contain only a small amount of precise marking data Training sample classification in obtain better nicety of grading.
Realizing the technical solution of the object of the invention is: firstly, using standard picture categorized data set mnist hand-written data Collection, but only to wherein 200 accurate training labels of data sample setting, then build a generation confrontation network of network and be arranged every Layer parameter is trained with training dataset to confrontation network is generated, and is obtained trained based on half prison for generating confrontation network Sorter network model is superintended and directed, finally image to be detected containing target is input in trained generation confrontation network model, is extracted The identification to target category is completed while target signature, implementation step includes the following:
(1) it chooses and downloads image classification standard exercise sample set, and carry out normalization operation；
(2) supervised learning parameter is set: i.e. first for the training number of tags of normalization training sample set setting supervised learning Amount；Reset the sample size of network training each time；Then it is set by the sample size of number of labels and each network training Determine the flag bit of supervised learning；
(3) it builds one and network is fought by the generation that generator network, arbiter network and subsidiary classification device form side by side, Wherein, generator network is set as 4 layers, and arbiter network is set as 5 layers, and subsidiary classification device is set as 5 layers；
(4) generation confrontation network is trained:
Training sample set after normalization is input to the arbiter network generated in confrontation network by (4a), extracts each sample The feature of initial target image in this, and all features are formed into characteristics of image figure, the last layer by arbiter network is defeated 1 unit vector out, for determining the classification of characteristic image；
(4b) receives a noise vector by generator network, and maps it onto one 28 × 28 characteristic pattern, then By this characteristic pattern and true training sample together as the input of arbiter network, the arbiter net after training for the first time is obtained Network parameter；
(4c) updates generator network parameter using the arbiter network parameter after training for the first time, then fixes updated Generator network parameter trains arbiter network again, successively repetitive exercise arbiter network and generator network；
(4d) utilizes generator gradient updating formula, and characteristic matching item is added, and generates after calculating repetitive exercise each time Device network weight；
(4e) utilizes supervised learning and unsupervised loss function, and each classification results value of computational discrimination device network and classification are tied Error amount between the true value of fruit；
(4f) utilizes stochastic gradient descent method, and increases label smooth operation to training sample set, updates generator network With the weight of each node of arbiter network, until arbiter network is for the nicety of grading of authentic specimen and false sample Reach nash banlance, obtains trained generation confrontation network model；
(5) classify to image to be detected:
Image containing target to be sorted is input in trained generation confrontation network model, the general of class option is exported Rate value, the highest class option of select probability value are exported as classification results.
The invention has the following advantages over the prior art:
First, improve the accuracy to image classification
Unsupervised training is only carried out in the prior art, does not have the training data of accurate label in training sample, it can not be accurate The boundary between class object is divided, and the present invention uses semi-supervised learning classification method, in original base for generating confrontation network Increase a subsidiary classification device on plinth and be used for image classification task, and inputs a small amount of precise marking data in training, these essences True flag data can more accurately divide the boundary between class object, to more accurately classify to image.
Second, need accurate exemplar quantity less
In existing semi-supervised training technique, it is still desirable to more accurate exemplar, but some practical application areas There is a problem of that accurate exemplar obtains difficulty, be unable to get a large amount of accurate labeled data, thus is lacking accurate enough mark It was easy to appear conjunction phenomenon in the case where note training sample, can not accurately complete image classification problem, and the present invention is using more The gradient of optimization declines update method, can compared with the prior art when processing contains the data sample of a small amount of flag data Reach more accurately image classification accuracy, possesses wide applicability.
Detailed description of the invention
Fig. 1 is implementation flow chart of the invention；
Fig. 2 is the simulation result diagram with the present invention to mnist data set,
Fig. 3 is the simulation result diagram with the present invention to cifar10 data set.
Specific embodiment
The embodiment of the present invention and effect are described in further detail with reference to the accompanying drawing.
Referring to Fig.1, to the specific steps of the present invention are as follows.
Step 1, it chooses and downloads image classification standard exercise sample set.
Standard picture categorized data set mnist hand-written data collection is downloaded, and operation is normalized to data sample, is used for Network model training；
Standard picture categorized data set cifar10 data set is downloaded, and operation is normalized to data sample, is used for net Network model training.
Step 2, supervised learning parameter is set.
The training samples number that training sample is concentrated is counted, the control total training samples number of supervised learning label data Zhan Percentage, the total training samples number percent value of label data Zhan is higher, and model training precision is better, but some practical applications are led It is difficult to obtain sufficient amount of accurate label data in domain, the specific total training samples number percent value of label data N Zhan is by reality Border applicable cases determine, choose 200 training samples to 50000 training samples in this example and are accurately marked, Zhan Zongxun Practicing sample percentage value is 0.4%；
According to Experimental Hardware performance and model training time comprehensive consideration, the sample size of each network training is set Size chooses each training samples number size=50 based on this Case Simulation platform；
By the sample size of training number of labels and each network training, the flag bit effective degree of supervised learning is calculated n；
N=N/size,
Wherein, n indicates the flag bit effective degree of supervised learning, and N indicates the accurate label data of supervised learning.
Step 3, a generation confrontation network is built.
(3a) builds the arbiter network being made of 4 layers of convolutional layer and 1 full articulamentum, and each layer parameter is as follows:
Level 1 volume lamination, the sum of Feature Mapping figure are set as 64, and the scale of convolution kernel is set as 5 × 5 sections Point, step-length 2, activation primitive are lrelu function；
Level 2 volume lamination, the sum of Feature Mapping figure are set as 128, and the scale of convolution kernel is set as 3 × 3 sections Point, step-length 2, activation primitive are lrelu function；
3rd layer of convolutional layer, the sum of Feature Mapping figure are set as 256, and the scale of convolution kernel is set as 3 × 3 sections Point, step-length 2, activation primitive are lrelu function；
4th layer of convolutional layer, the sum of Feature Mapping figure are set as 256, and the scale of convolution kernel is set as 3 × 3 sections Point, step-length 2, activation primitive are lrelu function；
5th layer is full articulamentum, and the sum of Feature Mapping figure is set as 11.
(3b) builds the generator network being made of 1 full articulamentum and 4 layers of convolutional layer, and each layer parameter is as follows:
First layer is full articulamentum, and the sum of Feature Mapping figure is set as 256；
Second layer convolutional layer, the sum of Feature Mapping figure are set as 256, and the scale of convolution kernel is set as 5 × 5 sections Point, step-length 2, activation primitive are relu function；
Third layer convolutional layer, the sum of Feature Mapping figure are set as 128, and the scale of convolution kernel is set as 5 × 5 sections Point, step-length 2, activation primitive are relu function；
4th layer of convolutional layer, the sum of Feature Mapping figure are set as 64, and the scale of convolution kernel is set as 5 × 5 sections Point, step-length 2, activation primitive are relu function；
(3c) increases a subsidiary classification device network on arbiter network, for inciting somebody to action in the classification task of data sample The classification task of K class data is extended to K+1 class data sorting task, wherein K+1 class sample is the void that generator network generates Fault image；
By above-mentioned arbiter network, generator network, subsidiary classification device network these three network parallel arrangeds, composition is generated Fight network.
Step 4, training generates confrontation network.
To generating the training of confrontation network at present mainly using the method for arbiter network and generator network alternately training, This step is also to be accomplished by using the method for arbiter network and the alternating training of generator network
Training sample set is input in the arbiter network generated in confrontation network by (4a), passes through arbiter network establishment 4 layers of convolutional layer, 4 convolution operations are carried out to the training sample set of input, extract each initial target image in each sample Feature, by all features constitute characteristics of image figure；
Noise vector is input in the generator network generated in confrontation network by (4b), passes through generator network establishment 1 full articulamentum and 4 layers of convolutional layer carry out 4 convolution operations to the training sample set of input, and generator network will export one The false sample of size identical as authentic specimen fixes the parameter value of each node of generator network for generator network at this time Input of the false picture and authentic specimen of generation together as arbiter network；
(4c) differentiates authentic specimen image and false sample image by arbiter network:
Using generator network losses function, generator network losses functional value is calculated:
tmin=EZ~P (z)Log (1-D (g (z))+f,
Wherein, tminIndicate generator network losses functional value, EZ~P (z)Indicate that sample obeys the mathematic expectaion of a certain distribution Value, z indicate that the noise vector of a certain distribution of obedience, D (G (z)) indicate that arbiter network generates image for generator network Differentiate that probability value, G (z) indicate that the sample image that generator network generates, f indicate characteristic matching value；
F={ [ni(r)-ni(f)]*[ni(r)-ni(f)] },
Wherein, ni(r) the true picture mapping value of the i-th layer network of arbiter, n are indicatedi(f) the i-th layer network of arbiter is indicated Vitua limage mapping value, optimize the gradient of generator network model by characteristic matching item f, so that arbiter network is each The result of layer is as similar as possible；
The loss function value of (4d) computational discrimination device；
(4d1) calculates the penalty values of supervised learning when supervised learning flag bit is effective by supervised learning loss function, Network inputs are the training sample with accurate label at this time, and the subsidiary classification device network in arbiter network, which exercises supervision, learns instruction Practice, calculates the network monitoring of subsidiary classification device and learn loss function value；
The subsidiary classification device supervised learning loss function is as follows:
Wherein, LsIndicate the loss function value of supervised learning,Indicate that sample obeys the mathematics phase of a certain distribution Prestige value, x indicate to obey the authentic specimen vector of a certain distribution, Pdata(x, y) indicates to obey the authentic specimen probability of a certain distribution Density function, the categorization values of y presentation class task, Pmodel(y | x, y < K+1) indicate K+1 class class probability model；
(4d2) calculates the damage of unsupervised learning by unsupervised learning loss function when supervised learning flag bit is invalid Mistake value, calculation formula are as follows:
Wherein, LunsIndicate the loss function value of unsupervised learning,Indicate that authentic specimen obeys a certain distribution Mathematical expectation, x indicate to obey the authentic specimen vector of a certain distribution, Pmodel(y=K+1 | x) it indicates to obey the general of a certain distribution Rate density model, EZ~noiseIndicate that false sample obeys the mathematical expectation of a certain distribution, z indicates to obey a certain distribution Noise vector.
(4d3) is according to supervised learning loss function value and unsupervised learning loss function value, the damage of computational discrimination device network Functional value is lost, calculation formula is as follows:
Lloss=Luns+Ls
Wherein, LlossIndicate the loss function value of arbiter network, LunsIndicate the damage of the unsupervised learning of arbiter network Lose functional value, LsIndicate the loss function value of the supervised learning of arbiter network.
(4e) utilizes stochastic gradient descent method, and the weight of each node of more newly-generated confrontation network convolutional layer obtains Trained generation fights network:
(4e1) selects a number at random in (0,0.1) range, uses the number as each node in generation confrontation network of network Initial weight；
The initial weight of each node is fought each node in network by (4e2) Current weight；
(4e3) randomly selects 50 sample images from training sample concentration and is input in arbiter network, and sets training Sample label, Lables indicate the data label value after unilateral label is smooth；
Lables=Lable*a
Wherein, Lable indicates the data label value of certain training in arbiter network, and a indicates unilateral label smoothing factor, Unilateral smoothing factor is set in this example as 0.9, and unilateral label smoothing method is added and makes arbiter classification boundaries smooth, prevents Arbiter easily finds the boundary distinguished between authentic specimen and false sample completely, reaches Continuous optimization arbiter gradient network Purpose；
The Vitua limage Mixed design that (4e4) generates authentic specimen and generator network into arbiter network, according to Arbiter network losses function seeks the current weight of each node in arbiter network by stochastic gradient descent method Local derviation obtains the gradient value of each node current weight in arbiter network；
(4e5) according to the following formula, the weight in computational discrimination device network after each node updates；
Wherein,Indicate the weight in arbiter network after t-th of node updates, StIt indicates in arbiter network t-th The current weight of node, ξ indicate learning rate, are set as 0.0001 in this example, Δ StIndicate t-th of node in arbiter network Current weight gradient value.
(4e6) executes (4e3) using the weight after each node updates as after current weight, and successively alternating iteration training is sentenced Other device network and generator network obtain trained generation confrontation network model until network reaches nash banlance.
Step 5, classify to detection image.
Image containing target to be sorted is input in trained generation confrontation network model, possible class label is exported Parameter is chosen the highest classification parameter of classification value parameter and is exported as classification results.
Effect of the invention is described further below with reference to emulation experiment.
1. emulation experiment condition:
The hardware test platform of emulation experiment of the present invention is: CPU intelCorei5-6500, dominant frequency 3.2GHz, interior Deposit 8GB, GPU NVIDIATITANXp；Software platform is: Ubuntu16.04LTS, 64 bit manipulation systems, python3.5.
2. emulation content analysis of simulation result:
Emulation 1, using method of the invention to 200 in 50000 training samples of mnist hand-written data collection accurate marks Note training sample and residue 49800 are emulated without label training sample, as a result such as Fig. 2, wherein result figure abscissa Iteration represents network training number, result figure ordinate testacc representative image nicety of grading.As it is clear from fig. 2 that this hair It is bright when 200 accurate mark samples are used only, the just image classification accuracy of acquirement 96%.
Emulation 2, using method of the invention to 200 in 50000 training samples of cifar10 data set accurate marks Training sample and residue 49800 are emulated without label training sample, as a result such as Fig. 3, wherein result figure abscissa Iteration represents network training number, result figure ordinate testacc representative image nicety of grading.It can be seen from figure 3 that this hair It is bright when 200 accurate mark samples are used only, the just image classification accuracy of acquirement 86%.
To sum up, 200 tape label data are only used only in 50000 training samples and can obtain preferably by the present invention Image classification accuracy significantly reduces the training set threshold of pattern classification task, possesses better applicability multi-field.

Claims (8)

1. a kind of based on the semi-supervision image classification method for generating confrontation network, which is characterized in that fight network using generating Minimax game reaches the nash banlance of network training；Using the subsidiary classification device network in arbiter network to detection mesh Mark completes classification task, realizes that step includes the following:
(1) it chooses and downloads image classification standard exercise sample set, and carry out normalization operation；
(2) supervised learning parameter is set: i.e. first for the training number of labels of normalization training sample set setting supervised learning；Again Set the sample size of network training each time；Then supervision is set by the sample size of number of labels and each network training The flag bit of study；
(3) it builds one and network is fought by the generation that generator network, arbiter network and subsidiary classification device form side by side, In, generator network is set as 5 layers, and arbiter network is set as 4 layers, and subsidiary classification device is set as 5 layers；
(4) generation confrontation network is trained:
Training sample set after normalization is input to the arbiter network generated in confrontation network by (4a), is extracted in each sample The feature of initial target image, and all features are formed into characteristics of image figure, 1 is exported by the last layer of arbiter network Unit vector, for determining the classification of characteristic image；
(4b) receives a noise vector by generator network, and maps it onto a characteristic pattern, then by this characteristic pattern Arbiter network parameter with true training sample together as the input of arbiter network, after obtaining training for the first time；
(4c) updates generator network parameter using the arbiter network parameter after training for the first time, then fixes updated generation Device network parameter trains arbiter network again, successively repetitive exercise arbiter network and generator network；
(4d) utilizes generator gradient updating formula, and characteristic matching item is added, and calculates generator net after repetitive exercise each time Network weight；
(4e) utilizes supervised learning and unsupervised loss function, each classification results value of computational discrimination device network and classification results Error amount between true value；
(4f) utilizes stochastic gradient descent method, and increases label smooth operation to training sample set, update generator network and sentence The weight of each node of other device network, until nicety of grading of the arbiter network for authentic specimen and false sample reaches Nash banlance obtains trained generation confrontation network model；
(5) classify to image to be detected:
Image containing target to be sorted is input in trained generation confrontation network model, the probability of class option is exported Value, the highest class option of select probability value are exported as classification results.
2. according to the method described in claim 1, wherein for the training of normalization training sample set setting supervised learning in (2) Number of labels is to control the percentage of the total training data of supervised learning label data Zhan, root by counting total training samples number The training number of labels of supervised learning is calculated according to percentages.
3. according to the method described in claim 1, wherein being set in (2) by the sample size of number of labels and each network training The flag bit for determining supervised learning, is accomplished by
One supervised learning symbol position flag is set to distinguish the sample training of supervised learning and unsupervised learning, works as progress When supervised learning training, setting supervised learning symbol position is effective status 1, the loss of arbiter network query function supervised learning Function, when carrying out unsupervised learning training, setting supervised learning symbol position is invalid state 0；
The loss function of arbiter network query function unsupervised learning, and pass through the sample of tape label sample size and each network training The flag bit effective degree of this quantity calculating supervised learning.
4. according to the method described in claim 1, the wherein arbiter network in (3), by 4 layers of convolutional layer and 1 layer of full articulamentum Composition, each layer parameter are as follows:
Level 1 volume lamination, the sum of Feature Mapping figure are set as 64, and the scale of convolution kernel is set as 5 × 5 nodes, step A length of 2, activation primitive is lrelu function；
Level 2 volume lamination, the sum of Feature Mapping figure are set as 128, and the scale of convolution kernel is set as 3 × 3 nodes, step A length of 2, activation primitive is lrelu function；
3rd layer of convolutional layer, the sum of Feature Mapping figure are set as 256, and the scale of convolution kernel is set as 3 × 3 nodes, step A length of 2, activation primitive is lrelu function；
4th layer of convolutional layer, the sum of Feature Mapping figure are set as 256, and the scale of convolution kernel is set as 3 × 3 nodes, step A length of 2, activation primitive is lrelu function；
5th layer of full articulamentum, the sum of Feature Mapping figure are set as 11.
5. according to the method described in claim 1, wherein generator network in (3), by 1 layer of full articulamentum and 4 layers of convolutional layer group At each layer parameter is as follows:
The full articulamentum of first layer, the sum of Feature Mapping figure are set as 256；
Second layer convolutional layer, the sum of Feature Mapping figure are set as 256, and the scale of convolution kernel is set as 5 × 5 nodes, Step-length is 2, and activation primitive is relu function；
Third layer convolutional layer, the sum of Feature Mapping figure are set as 128, and the scale of convolution kernel is set as 5 × 5 nodes, Step-length is 2, and activation primitive is relu function；
4th layer of convolutional layer, the sum of Feature Mapping figure are set as 64, and the scale of convolution kernel is set as 5 × 5 nodes, step A length of 2, activation primitive is relu function；
Layer 5 convolutional layer, the sum of Feature Mapping figure are set as 1, and the scale of convolution kernel is set as 1 × 1 node, step A length of 1, activation primitive is relu function.
6. structure is identical as arbiter network, is used for according to the method described in claim 1, wherein subsidiary classification device in (3) The classification task of K class data is extended to K+1 class data sorting task in the classification task of data sample, wherein K+1 class Data are the Vitua limages that generator network generates.
7. leading to according to the method described in claim 1, wherein calculating generator network weight after repetitive exercise each time in (4d) Following formula is crossed to carry out:
EZ~P (z)Log (1-D (G (z))),
Wherein, EZ~P (z)Indicate that sample obeys the mathematical expectation of a certain distribution, z indicates to obey the noise vector of a certain distribution, P (z) indicate that the noise probability density function of a certain distribution of obedience, D (G (z)) indicate that arbiter network generates generator network The differentiation probability value of image, G (z) indicate the sample image that generator network generates.
8. according to the method described in claim 1, wherein calculating each classification results value of arbiter network and classification knot in (4e) Error amount between the true value of fruit realizes that steps are as follows:
The loss function value of (4e1) calculating supervised learning:
Wherein, LsIndicate the loss function value of supervised learning,Indicate that sample obeys the mathematical expectation of a certain distribution, X indicates to obey the authentic specimen vector of a certain distribution, Pdata(x, y) indicates to obey the authentic specimen probability density letter of a certain distribution Number, the categorization values of y presentation class task, Pmodel(y | x, y < K+1) indicate K+1 class class probability model.
The loss function value of (4e2) calculating unsupervised learning:
Wherein, LunsIndicate the loss function value of unsupervised learning,Indicate that authentic specimen obeys the mathematics of a certain distribution Desired value, x indicate to obey the authentic specimen vector of a certain distribution, Pmodel(y=K+1 | x) indicate that the probability for obeying a certain distribution is close Spend model, EZ~noiseIndicate that false sample obeys the mathematical expectation of a certain distribution, z indicates to obey the noise of a certain distribution Vector；
(4e3) is according to (4e1) and (4e2) as a result, the error amount L of computational discrimination device networkloss:
Lloss=Luns+Ls。
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Application publication date: 20190806