CN110880019A - Method for adaptively training target domain classification model through unsupervised domain - Google Patents

Method for adaptively training target domain classification model through unsupervised domain Download PDF

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CN110880019A
CN110880019A CN201911045261.4A CN201911045261A CN110880019A CN 110880019 A CN110880019 A CN 110880019A CN 201911045261 A CN201911045261 A CN 201911045261A CN 110880019 A CN110880019 A CN 110880019A
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张勇东
张天柱
钱柄乔
李岩
邓旭冉
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Beijing Academy Of Chinese Studies
University of Science and Technology of China USTC
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Abstract

The invention discloses a method for adaptively training a classification model of a target domain through an unsupervised domain, which comprises the following steps: extracting features of batch image data input by a source domain and a target domain through a standard convolution network, and then constructing an example graph corresponding to the source domain and the target domain by combining with the initially set centroid features; after the node matrix in the example graph sequentially passes through the source domain classifier and the graph convolution network, category centroid characteristics corresponding to a source domain and a target domain are generated; using a class centroid alignment mechanism to constrain class centroids from different domains in each layer of the graph convolution network, so that the class centroids of different domains are gradually close along with iterative training; moreover, a antagonism alignment mechanism guided by the mass center is used, and the mass center automatically generated by all categories is used as the global domain statistical information to guide each batch of image data to participate in antagonism training; and finally obtaining a classification model effective in the target domain through iterative training. The method has good generalization and the trained classification model has high classification accuracy.

Description

Method for adaptively training target domain classification model through unsupervised domain
Technical Field
The invention relates to the technical field of image classification, in particular to a method for adaptively training a target domain classification model through an unsupervised domain.
Background
Unsupervised domain adaptation can utilize existing source domain tagged data and network models and associated target domain untagged data to learn to derive a network model suitable for target domain data classification.
Conventional unsupervised domain adaptation methods typically use measures such as correlation distance metrics to align the data distribution of the source domain and the target domain output by the deep network. In recent years, many adversarial domain adaptation methods have been proposed and achieved remarkable results, and most of these methods are based on generation of an adversarial network. The method mainly comprises the steps of training a discriminator to discriminate whether a sampling feature is from a source domain or a target domain, and simultaneously training a feature extractor to deceive the discriminator, so that the feature distributions of the source domain and the target domain are aligned and cannot be distinguished.
Most of these methods focus on measuring domain differences at the domain level, without distinguishing whether samples from two domains are aligned according to the category to which they belong. Even if the global statistics are completely mixed up, the difference between the source domain and the target domain is not necessarily reduced, and even different types of samples are mixed together, so that the classification effect is still to be improved.
Disclosure of Invention
The invention aims to provide a method for adaptively training a target domain classification model through an unsupervised domain, which has good generalization and the classification accuracy of the trained classification model is higher.
The purpose of the invention is realized by the following technical scheme:
a method of training a target domain classification model through unsupervised domain adaptation, comprising:
extracting image characteristics of batch image data input by a source domain and a target domain through a standard convolution network, and then constructing an example graph corresponding to the source domain and the target domain by combining with the mass center characteristics set by initialization; after the node matrix in the example graph sequentially passes through the source domain classifier and the graph convolution network, the class centroid characteristics corresponding to the source domain and the target domain are updated;
using a class centroid feature alignment mechanism to constrain class centroid features from different domains in each layer of the graph convolution network, so that the class centroid features of different domains gradually approach along with iterative training;
moreover, a antagonism alignment mechanism guided by the centroid is used, and the centroid characteristics automatically generated by all categories are used as domain global statistical information to guide each batch of image data to participate in antagonism training;
and finally obtaining a classification model effective in the target domain through iterative training.
According to the technical scheme provided by the invention, the network can be trained in an end-to-end mode to automatically learn the class centroid without depending on specific prior knowledge, so that the method has better generalization; according to the method, the average classification accuracy rate is improved by 1-2% on a plurality of data sets, and the convergence rate of the model classification accuracy rate is higher.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a block diagram of a method for training a target domain classification model through unsupervised domain adaptation according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a method for training a target domain classification model through unsupervised domain adaptation, the unsupervised domain adaptation related by the method is based on graph convolution category perception structure modeling, as shown in figure 1, the unsupervised domain adaptation is an integral framework of a related method, and the training process mainly comprises the following parts:
1. and automatically generating class centroid characteristics.
Extracting image characteristics of batch image data input by a source domain and a target domain through a standard convolution network, and then constructing an example graph corresponding to the source domain and the target domain by combining with initially set class centroid characteristics; the example graph comprises a node matrix and a corresponding adjacency matrix, wherein the node matrix is composed of a series of characteristic nodes, and after the node matrix sequentially passes through the source domain classifier and the graph convolution network, the class centroid characteristics corresponding to the source domain and the target domain are updated.
In an embodiment of the present invention, the batch image data input by the source domain and the target domain includes: source domain data with a label and target domain data without a label.
Extracting image features through a standard convolution network, and then constructing a node matrix corresponding to a source domain and a target domain by combining with the initially set centroid features to represent as follows:
Figure BDA0002253960470000031
in the above formula, AlexNet (X)batch) Representing extraction of batch image data X using AlexNet Standard convolutional networkbatchThe features of (1); c represents the category centroid characteristics of the initialization setting;
Figure BDA0002253960470000032
representing a characteristic concatenation; s, T, corresponding to the source and target domains, respectively, i.e.
Figure BDA0002253960470000033
A matrix of nodes corresponding to the source domain,
Figure BDA0002253960470000034
a node matrix corresponding to the target domain.
As shown in FIG. 1, wherein XS、XTFeatures of the extracted source domain and target domain images.
Predicting a node matrix using a source domain classifier
Figure BDA0002253960470000035
Soft label of each characteristic node
Figure BDA0002253960470000036
Therefore, the weights of the disconnected edges of the corresponding characteristic nodes and the corresponding adjacent matrixes are constructed according to the similarity among the characteristic nodes
Figure BDA0002253960470000037
Expressed as:
Figure BDA0002253960470000038
in the above formula, ═ S.T,
Figure BDA0002253960470000039
respectively are an adjacent matrix corresponding to the node matrix of the source domain and a soft label of the source domain characteristic node;
Figure BDA00022539604700000310
respectively is an adjacent matrix corresponding to the node matrix of the target domain and a soft label of the characteristic node of the target domain.
The node matrix and the corresponding adjacent matrix obtained in the above way form a complete example graph.
Then, the node matrixes of the source domain and the target domain respectively pass through a graph convolution network, and the related operation is expressed as follows:
Figure BDA00022539604700000311
in the above formula, the first and second carbon atoms are,
Figure BDA00022539604700000312
correspondingly representing the output results of the ith layer and the l +1 layer of the graph convolution network;
Figure BDA00022539604700000313
a degree matrix with ith row and ith column elements
Figure BDA00022539604700000314
Figure BDA00022539604700000315
Is a contiguous matrix
Figure BDA00022539604700000316
Row i and column j, similarly, T corresponds to source and destination domains, W(l+1)The learnable parameters of the l +1 th layer of graph convolution are represented, the parameters in the graph convolution network corresponding to the source domain and the target domain are shared, and sigma represents an activation function.
And transmitting the feature information by using the relationship between the feature nodes, updating the node features, automatically generating new class centroid features and updating the original class centroid features. While the two alignment mechanisms described below for class centroid features will constrain the features to be semantically rich.
In the embodiment of the present invention, since the image samples are to be classified accurately, for each class, there exists a class centroid feature in the feature space of the features of all the image samples to represent all the image samples of the class. Class centroid features do not actually exist inIn the conventional method, the centroid feature is represented by averaging the same-class sample features, in the embodiment of the invention, the class centroid feature is automatically learned by using a graph convolution network, as shown in fig. 1, C is shown on the left side and the right sideSAnd CTBoth represent the class centroid characteristics generated by the source domain and the target domain, with the difference being that the left side is the pre-update class centroid characteristic and the right side is the post-update class centroid characteristic. As will be understood by those skilled in the art, the class centroid feature before updating is the class centroid feature set by the initialization, or the class centroid feature obtained after the last operation.
2. Class centroid feature alignment mechanism.
In practice, the conventional method focuses on modeling class-level information, so that impressive effects are obtained, and the importance of the class-level information is further emphasized. In order to ensure that features from the same class in different domains are mapped to adjacent locations, in an embodiment of the invention, a class centroid feature alignment mechanism is designed to model unsupervised domain adapted class information by constraining class centroid features from different domains in each layer of the graph convolution network such that the class centroid features of different domains come closer together as iterative training progresses, in such a way as to encode the learned features. Thus, samples belonging to the same class can be embedded in nearby locations in the feature space.
The penalty function for the class centroid feature alignment mechanism is:
Figure BDA0002253960470000041
wherein K represents the number of categories, phi represents the distance metric function, K is the category number, CSAnd CTRespectively representing class centroid characteristics of the source domain and the target domain.
3. Centroid directed antagonism alignment mechanism.
In the embodiment of the invention, a centroid-guided antagonism alignment mechanism is used, and the centroid characteristics automatically generated by all categories are used as domain global statistical information to guide each batch of image data to participate in antagonism training, so that the influence of local information interference discriminators on domain global distribution judgment brought by batch sample input is relieved. The participation of class centroid features may improve training efficiency and adaptability.
The loss function for the centroid directed antagonistic alignment mechanism is:
Figure BDA0002253960470000042
wherein, χS＝{x|x∈DS}，χT＝{x|x∈DTRespectively representing source domain and target domain images, CSAnd CTClass centroid features representing the source domain and the target domain, respectively, D representing the discriminator, G representing the feature extractor,
Figure BDA0002253960470000043
representing a series of features.
As shown in fig. 1, the feature extractor is composed of a standard convolution network AlexNet and a graph convolution network.
As shown in fig. 1, the classifier F is a task-specific classifier (training classifier F for short) updated with training for the domain-adapted target, and after the training of the classification model is completed, the training classifier F is used to classify the target domain picture and output a classification score. The discriminator D is used in the training phase, and its main role is to determine whether the image is from the source domain or the target domain according to the input features and the class centroid features, which can be expressed as: d (Concat ([ X ]S，CS]，axis＝0))，D(Concat([XT，CT]Axis ═ 0)), Concat denotes a splicing operation, and axis ═ 0 denotes that Concat was performed on the 0 th dimension.
The antagonism alignment mechanism designed by the scheme of the embodiment of the invention has participation of the class centroid features, thereby helping the alignment of the class centroid features. In turn, the class centroid feature alignment mechanism constrains learning more semantic class centroid features to help guide the antagonism alignment mechanism. Therefore, the two mechanisms can be mutually strengthened, so that the classification model can be trained in an end-to-end mode without depending on human priors.
Through the mode, the defined loss function is combined for continuous iterative training, and finally the classification model effective in the target domain can be obtained. The classification model obtained by training can effectively classify the input target domain images, and the classification accuracy is high. In the testing stage, the target field image chiTInputting a trained classification model, extracting features through a feature extractor G, namely AlexNet, and combining the training to obtain the target domain centroid features
Figure BDA0002253960470000051
Further extracted feature G (χ) by GCNT) I.e. by
Figure BDA0002253960470000052
Extracting the final characteristic G (χ)T) And inputting the result into a training classifier F to obtain a classification result.
The scheme of the embodiment of the invention can be applied to the pre-classification of large-data-scale label-free images. In practice, the image data processing system can be installed on a working computer in a software mode to perform real-time classified display of small image data batches, and can also be installed on a large server to process large image data batches.
Compared with the prior art, the technical effects are mainly obtained as follows:
the network can be trained in an end-to-end mode to automatically learn the class centroid characteristics without depending on specific prior knowledge of human, so that the method has better generalization; according to the method, the average classification accuracy rate is improved by 1-2% on a plurality of data sets, and the convergence rate of the model classification accuracy rate is higher.
Through the above description of the embodiments, it is clear to those skilled in the art that the above embodiments can be implemented by software, and can also be implemented by software plus a necessary general hardware platform. With this understanding, the technical solutions of the embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments of the present invention.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. A method for training a classification model of a target domain through unsupervised domain adaptation, comprising:
extracting image characteristics of batch image data input by a source domain and a target domain through a standard convolution network, and then constructing an example graph corresponding to the source domain and the target domain by combining with the mass center characteristics set by initialization; after the node matrix in the example graph sequentially passes through the source domain classifier and the graph convolution network, the class centroid characteristics corresponding to the source domain and the target domain are updated;
using a class centroid feature alignment mechanism to constrain class centroid features from different domains in each layer of the graph convolution network, so that the class centroid features of different domains gradually approach along with iterative training;
moreover, a antagonism alignment mechanism guided by the centroid is used, and the centroid characteristics automatically generated by all categories are used as domain global statistical information to guide each batch of image data to participate in antagonism training;
and finally obtaining a classification model effective in the target domain through iterative training.
2. The method of claim 1, wherein the batch of image data input by the source domain and the target domain comprises: source domain data with a label and target domain data without a label.
3. The method of claim 1, wherein the extracting image features through a standard convolutional network and then constructing the instance graph corresponding to the source domain and the target domain in combination with the initially set centroid features comprises:
extracting image features, and constructing a node matrix corresponding to a source domain and a target domain by combining with the initially set centroid features:
Figure FDA0002253960460000011
in the above formula, AlexNet (X)batch) Representing extraction of batch image data X using AlexNet Standard convolutional networkbatchThe features of (1); c represents the category centroid characteristics of the initialization setting;
Figure FDA0002253960460000012
representing a characteristic concatenation; s, T, corresponding to the source and target domains, respectively, i.e.
Figure FDA0002253960460000013
A matrix of nodes corresponding to the source domain,
Figure FDA0002253960460000014
a node matrix corresponding to the target domain;
predicting node matrices by source domain classifier
Figure FDA0002253960460000015
Soft label of each characteristic node
Figure FDA0002253960460000016
Therefore, the weights of the disconnected edges of the corresponding characteristic nodes and the corresponding adjacent matrixes are constructed according to the similarity among the characteristic nodes
Figure FDA0002253960460000017
Expressed as:
Figure FDA0002253960460000018
wherein, S, T,
Figure FDA0002253960460000019
respectively are an adjacent matrix corresponding to the node matrix of the source domain and a soft label of the source domain characteristic node;
Figure FDA00022539604600000110
respectively are an adjacent matrix corresponding to the node matrix of the target domain and a soft label of the characteristic node of the target domain;
the node matrix obtained in the above manner and the corresponding adjacency matrix form an example graph.
4. The method of claim 3, wherein the node matrices of the source domain and the target domain are each represented by a graph convolution network, and the correlation operation is represented as:
Figure FDA0002253960460000021
in the above formula, the first and second carbon atoms are,
Figure FDA0002253960460000022
correspondingly representing the output results of the ith layer and the l +1 layer of the graph convolution network;
Figure FDA00022539604600000210
a degree matrix with ith row and ith column elements
Figure FDA0002253960460000024
Figure FDA00022539604600000211
Is a contiguous matrix
Figure FDA0002253960460000025
Row ith and column jth element, W(l+1)The learnable parameters of the l +1 th layer of graph convolution are represented, the parameters in the graph convolution network corresponding to the source domain and the target domain are shared, and sigma represents an activation function.
5. The method of claim 1, wherein the loss function of the class centroid feature alignment mechanism is:
Figure FDA0002253960460000026
wherein K represents the number of categories, phi represents the distance metric function, K is the category number, CSAnd CTRespectively representing class centroid characteristics of the source domain and the target domain.
6. The method of claim 1, wherein the loss function of the centroid-directed antagonism alignment mechanism is:
Figure FDA0002253960460000027
wherein the content of the first and second substances,
Figure FDA0002253960460000028
representing source and target domain images, respectively, CSAnd CTRespectively representing the class centroid characteristics of a source domain and a target domain, D representing a discriminator, G representing a characteristic extractor, and consisting of a standard convolution network and a graph convolution network,
Figure FDA0002253960460000029
representing a series of features.
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