CN115017351A - Light-weight industrial picture classification method and system based on federal small sample learning - Google Patents
Light-weight industrial picture classification method and system based on federal small sample learning Download PDFInfo
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Abstract
A lightweight industrial picture classification method and system based on federal small sample learning comprise the following steps: modeling an industrial picture classification system based on a federal small sample learning framework; task for classifying small sample picturesModeling is carried out; build by embedding module l θ And relation module g φ A small sample classifier f based on a lightweight relational network is formed; picture classification task based on small sampleConstruction ofA training method of a small sample classifier f; constructing a side cloud collaborative deployment and training method of the small sample classifier based on a small sample classifier training method; and performing model reasoning locally based on the edge cloud collaborative deployment and training method of the small sample classifier. The invention fully utilizes the heterogeneous and mutually isolated limited sample sets of different industrial mechanisms to realize the effective training of the overall small sample picture classifier, is also suitable for external clients holding samples which are not similar during the training period, and supports the industrial mechanisms with limited samples to establish the data-oriented picture classifier.
Description
Technical Field
The invention belongs to the field of distributed intelligence, and particularly relates to a method and a system for classifying light-weight industrial pictures based on federal small sample learning.
Background
Currently, Deep Convolutional Neural Network (DCNN) based picture classification is widely applied in different industrial scenes. In the existing method, a DCNN classifier which is intensive in calculation is deployed at the cloud, and each industrial mechanism uploads an original picture to be classified. However, this approach may reveal the privacy of the industrial establishment. In addition, cloud brings high network communication burden on large-scale industrial pictures. In order to solve the problem, the federal learning technology is generally concerned, and for the problem of industrial picture classification, different industrial institutions can serve as clients to uniformly coordinate and cooperatively train a global DCNN classifier through the cloud on the premise of not uploading original pictures. However, the sample scarcity problem (including insufficient sample number and sample class isolation) faced by practical industrial institutions has limited the application of existing data-intensive federal learning methods in industrial picture classification scenarios. Aiming at the problem of sample scarcity, each industrial mechanism can adopt the existing single-machine small sample learning technology to obtain a small sample picture classifier based on limited data training locally, but due to strict data isolation among the industrial mechanisms, the model obtained by training cannot be generalized on other types of samples, and under the condition, the industrial mechanism with extremely limited samples cannot establish an available picture classifier. In addition, practical industrial institutions often have heterogeneous and limited computing and network resources, and cannot effectively support the existing resource-intensive DCNN image classification method.
Disclosure of Invention
The invention aims to provide a federate small sample learning-based lightweight industrial picture classification method and system, and aims to solve the problems that an industrial mechanism with extremely limited samples cannot establish an available picture classifier, and an actual industrial mechanism usually has heterogeneous and limited computing and network resources and cannot effectively support the existing resource-intensive DCNN picture classification method.
In order to achieve the purpose, the invention adopts the following technical scheme:
the method for classifying the light-weight industrial pictures based on the federal small sample learning comprises the following steps:
modeling is carried out on an industrial picture classification system based on a federal small sample learning framework, wherein the system comprises a cloud server c and a group of industrial institutions epsilon with the same interest, and each e i E epsilon holds respective local sample sets separately
For each industrial mechanism in the constructed system model, classifying the small sample picturesModeling is performed, each task comprising a support setAnd query set
Build by embedding module l θ And relation module g φ The small sample classifier f based on the lightweight relational network is formed, wherein theta and phi are parameters of functions l and g;
picture classification task based on small sampleConstructing a training method of a small sample classifier f;
constructing a side cloud collaborative deployment and training method of the small sample classifier based on a small sample classifier training method, wherein the complete small sample image classifier based on a lightweight relation network is deployed on c, and an embedded module part is arrangedIs deployed at all e i E, on epsilon, training the classifier based on the training method in the step 4 along a discrete time point T of 0,1, … and T, wherein T is the iteration number of a preset model;
edge cloud collaborative deployment and training method based on small sample classifier, and small sample classifier deployed and trained to be constructedAfter training is completed, each external client is initializedModel (2)Model reasoning is performed locally.
Furthermore, a federal small sample learning technology is utilized to model an industrial picture classification system, privacy of each industrial organization is guaranteed, meanwhile, training of a global small sample picture classifier of an industrial picture island which is heterogeneous and sample-limited is supported, and the obtained global picture classifier is suitable for not only federal model training participation clients, but also external clients holding unseen samples during training.
Further, the specific operation is as follows: the industrial picture classification system modeling based on the federal small sample learning comprises a cloud server c and a group of industrial institutions epsilon-e with the same interest i Wherein Each e i E epsilon holds respective local sample sets separatelyWherein e is i Hold withThe class samples are, for example, is e i Class j samples of (1), for all e i ,e j ∈ε,i≠j,c can receive messages sent by epsilon but cannot access the sample set of epsilon, epsilon can receive messages sent by c but the interior of epsilon cannot be communicated, and a federal small sample picture classifier is set to train and participate in a client setExternal client set
Further, the specific operation of modeling the small sample picture classification task T is as follows: c-way K-shot picture classification task on classifier fIncluding support setsAnd query set The sample containing C x K labeled samples,containing C × K' samples to be classified (, requirement f is based onFor is toAnd (6) classifying.
Furthermore, a small sample image classifier f based on the lightweight relational network is constructed by utilizing a relational network technology.
Further, the specific operation is as follows: constructing a multilayer CNN model f by an embedded module l θ And relation module g φ Composition, where θ, φ are parameters of functions l, g, l θ Extracting a sample feature map of an input image, wherein the sample feature map comprises two convolution blocks, each convolution block comprises 32 convolution layers with 3 multiplied by 3 filters, a batch normalization layer, a ReLU nonlinear layer and a 2 multiplied by 2 maximum pooling layer; g φ And determining whether two input samples belong to the same class or not based on the similarity of the characteristic graphs, wherein the two input samples comprise the two volume blocks, an 8-dimensional full-connection layer based on the ReLU and a 1-dimensional full-connection layer based on the Sigmoid, and for single-channel and three-channel images, the output size H of the last maximum pooling layer is 32 and 32 multiplied by 3.
Further, the training method of the small sample classifier f specifically operates as follows: small sample picture classification task based onFor any sample pairObtaining a feature map l based on an embedding module θ (x a )、l θ (x b ) Computing a relationship score based on the relationship moduleWherein,for feature graph join operations, relationship scores for all sample pairs are organized using a relationship matrix, with classifier lossWherein:
updating model parameters theta by random gradient descent * ,φ * 。
Further, a side cloud collaborative deployment and training method of the small sample picture classifier based on the lightweight relational network is constructed by using a side cloud collaborative intelligent technology.
Further, the method comprises the following steps:
step 5.1, setting the current iteration times t ← 0, and initializing a cloud modelWherein theta is init 、φ init Initializing each participating client for random initialization parametersModel parameters ofEach participating client e i ∈ε i Organizing local task setsUpdating the current iteration time t ← 1;
step 5.2, each participating client e i ∈ε i FromRandom extraction taskObtaining all sample pairsCharacteristic diagram ofUpload all featuresSignature and corresponding sample label to c;
step 5.3, for each participating client e i ∈ε i Based on step 4, c calculationUpdatingWhere α is the random gradient descent learning rate, given to each participating client e i ∈ε i Return theta g (t+1);
Step 5.5, updating the current iteration time T ← T +1, and if T is less than or equal to T, repeating the steps from 5.2 to 5.4;
Further, a federal small sample learning-based lightweight industrial picture classification system includes:
the system modeling module is used for modeling an industrial picture classification system based on a federal small sample learning framework, and comprises a cloud server c and a group of industrial institutions epsilon with the same interest, and each e i E epsilon holds respective local sample sets separately
A small sample picture classification task modeling module used for classifying small sample pictures for each industrial mechanism in the constructed system modelModeling is performed, each task comprising a support setAnd query set
A classifier building module for building a classifier from the embedded module θ And relation module g φ The small sample classifier f based on the lightweight relational network is formed, wherein theta and phi are parameters of functions l and g;
a training module for classifying tasks based on small sample picturesConstructing a training method of a small sample classifier f; constructing an edge cloud collaborative deployment and training method of the small sample classifier based on a small sample classifier training method, wherein the complete small sample image classifier based on a lightweight relational network is deployed on c, and an embedded module is deployed on all e i E, on epsilon, training the classifier based on the training method in the step 4 along a discrete time point T of 0,1, … and T, wherein T is the iteration number of a preset model;
the output reasoning module is used for deploying and training the constructed small sample classifier based on the edge cloud collaborative deployment and training method of the small sample classifierAfter training is completed, each external client is initializedModel (2)Model reasoning is performed locally.
Compared with the prior art, the invention has the following technical effects:
according to the invention, by establishing an industrial picture classification system model based on federal small sample learning, while not invading the privacy of industrial institutions, the effective training of a global small sample picture classifier is realized by fully utilizing heterogeneous and mutually isolated limited sample sets of different industrial institutions, and the obtained global model is not only suitable for clients participated in federal model training, but also suitable for external clients holding samples which are not classified during training, and supports the industrial institutions with limited samples to establish the data-oriented picture classifier.
Furthermore, by constructing the small sample picture classifier based on the lightweight relational network, the calculation and storage overhead of an industrial mechanism with resource limitation in the model training process is obviously reduced by utilizing a lighter model structure design while the model classification precision is ensured.
Furthermore, by establishing a side cloud collaborative deployment and training method of the small sample picture classifier based on the lightweight relational network, the model splitting precision is guaranteed, and meanwhile, only a communication lightweight embedded module is deployed at the client side of the industrial mechanism, so that the communication overhead of the industrial mechanism with resource limitation in the model training process is remarkably reduced.
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FIG. 1 is a schematic representation of an implementation of the process herein;
FIG. 2 is a logic flow diagram of the method herein;
Detailed Description
The present invention is described in further detail below with reference to the attached drawing figures.
Referring to fig. 1, the invention provides a lightweight industrial picture classification method based on federal small sample learning, which comprises the following steps:
step 3, the small sample picture classifier based on the lightweight relational network is a multilayer CNN model f and is formed by an embedded module l θ And relation module g φ Composition, where θ, φ are parameters of functions l, g, l θ Extracting a sample feature map of an input image, wherein the sample feature map comprises two convolution blocks, each convolution block comprises 32 convolution layers with 3 multiplied by 3 filters, a batch normalization layer, a ReLU nonlinear layer and a 2 multiplied by 2 maximum pooling layer; g φ Determining whether two input samples belong to the same class or not based on the similarity (relationship score) of the feature map, wherein the two input samples comprise the two volume blocks, an 8-dimensional full-connection layer based on the ReLU and a 1-dimensional full-connection layer based on the Sigmoid, and for single-channel and three-channel images, the output size H of the last maximum pooling layer is 32 and 32 x 3;
step 4, training the small sample picture classifier based on the lightweight relation network, and classifying the task based on the small sample picture in the step 2For any sample pairObtaining a feature map l based on an embedding module θ (x a )、l θ (x b ) Computing a relationship score based on the relationship moduleWherein,for feature graph join operations, relationship scores for all sample pairs are organized using a relationship matrix, with classifier penaltiesWherein:
updating model parameters theta by random gradient descent * ,φ * ;
Step 5, edge cloud collaborative deployment and training of the small sample picture classifier based on the lightweight relational network, in order to reduce calculation, communication and storage expenses of a client in the Federal model training process, the complete small sample picture classifier based on the lightweight relational network is deployed on the c, and the embedded module is deployed on all e i E epsilon, training the classifier along the discrete time point T of 0,1, …, T, wherein T is the preset iteration number of the model, and the step 5 comprises the following steps:
step 5.1, setting the current iteration times t ← 0, and initializing a cloud modelWherein theta is init 、φ init Initializing each participating client for random initialization parametersModel parameters ofEach participating client e i ∈ε i Organizing local task setsUpdating the current iteration time t ← 1;
step 5.2, each participating client e i ∈ε i FromRandom extraction taskBased on step 4, all samples were obtainedBook pairCharacteristic diagram ofUploading all feature maps and corresponding sample labels to c;
step 5.3, for each participating client e i ∈ε i Based on step 4, c calculationUpdatingWherein alpha is a random gradient descent learning rate, and gives each participating client e i ∈ε i Return theta g (t+1);
Step 5.5, updating the current iteration time T ← T +1, and if T is less than or equal to T, repeating the steps from 5.2 to 5.4;
Referring to fig. 2, the invention provides a lightweight industrial picture classification method based on federal small sample learning, and a logic architecture body of the method comprises modeling of an industrial picture classification system based on federal small sample learning, construction of a small sample picture classifier based on a lightweight relational network, and edge cloud collaborative deployment and training of the small sample picture classifier based on the lightweight relational network. In order to solve the problem that an industrial mechanism with limited samples cannot effectively establish a data-driven industrial picture classifier, an industrial picture classification system based on federal small sample learning is established, and global small sample picture classifier training of an industrial picture island which is heterogeneous and has limited samples is supported; in order to reduce the calculation and storage cost of an industrial mechanism with limited resources in the model training process, a small sample picture classifier based on a lightweight relational network is constructed; in order to reduce communication overhead of an industrial mechanism with limited resources in the model training process, a side cloud collaborative deployment and training method of a small sample picture classifier based on a lightweight relational network is established.
In another embodiment of the present invention, a light-weight industrial picture classification system based on federal small sample learning is provided, which can be used to implement the light-weight industrial picture classification method based on federal small sample learning described above, and specifically, the system includes:
the system modeling module is used for modeling an industrial picture classification system based on a federal small sample learning framework, and comprises a cloud server c and a group of industrial institutions epsilon with the same interest, and each e i Separately holding respective local sample sets
A small sample picture classification task modeling module used for classifying small sample pictures for each industrial mechanism in the constructed system modelModeling is performed, each task comprising a support setAnd query set
A classifier building module for building a classifier from the embedded module θ And relation module g φ The small sample classifier f based on the lightweight relational network is formed, wherein theta and phi are parameters of functions l and g;
a training module for classifying tasks based on small sample picturesConstructing a training method of a small sample classifier f; constructing an edge cloud collaborative deployment and training method of the small sample classifier based on a small sample classifier training method, wherein the complete small sample image classifier based on a lightweight relational network is deployed on c, and an embedded module is deployed on all e i E, on epsilon, training the classifier based on the training method in the step 4 along a discrete time point T of 0,1, … and T, wherein T is the iteration number of a preset model;
the output reasoning module is used for deploying and training the constructed small sample classifier based on the edge cloud collaborative deployment and training method of the small sample classifierAfter training is completed, each external client is initializedModel (2)Model reasoning is performed locally.
The invention solves the problem that an available data-driven picture classifier cannot be established by a scarce industrial mechanism with sample isolation under the requirement of strong privacy protection. The invention can effectively train the global small sample picture classifier by crossing heterogeneous industrial picture islands with limited samples on the premise of ensuring the privacy of industrial institutions, and the obtained global model is not only suitable for clients participated in the federal model training, but also suitable for external clients holding samples which are not classified during the training. The small sample picture classifier based on the lightweight relational network and the edge cloud collaborative deployment and training method thereof can ensure the classification precision of the model and simultaneously remarkably reduce the calculation, communication and storage expenses of the resource-limited industrial mechanism.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. The method for classifying the light-weight industrial pictures based on the federal small sample learning is characterized by comprising the following steps of:
modeling an industrial picture classification system based on a federal small sample learning framework, wherein the system comprises a cloud server c and a group of industrial institutions epsilon with same interests, and each e i E epsilon holds respective local sample sets separately
For each industrial mechanism in the constructed system model, classifying the small sample picturesModeling is performed, each task comprising a support setAnd query set
Build by embedding module l θ And relation module g φ The small sample classifier f based on the lightweight relational network is formed, wherein theta and phi are parameters of functions l and g;
picture classification task based on small sampleConstructing a training method of a small sample classifier f;
constructing an edge cloud collaborative deployment and training method of the small sample classifier based on a small sample classifier training method, wherein the complete small sample image classifier based on a lightweight relational network is deployed on c, and an embedded module is deployed on all e i E, on epsilon, training the classifier based on the training method in the step 4 along a discrete time point T of 0,1, … and T, wherein T is the iteration number of a preset model;
2. The lightweight industrial picture classification method based on federal small sample learning as claimed in claim 1, wherein the federal small sample learning technology is used for modeling an industrial picture classification system, privacy of each industrial institution is guaranteed, training of a global small sample picture classifier of an industrial picture island which is heterogeneous and has limited samples is supported, and the obtained global picture classifier is suitable for both federal model training participating clients and external clients holding unseen samples during training.
3. The method for classifying a lightweight industrial picture based on federal small sample learning as claimed in claim 2, wherein the specific operation is: the industrial picture classification system modeling based on the federal small sample learning comprises a cloud server c and a group of industrial institutions with the same interest, wherein the industrial institutions are epsilon or e i Wherein Each e i E epsilon holds respective local sample sets separatelyWherein e is i Hold withThe class samples are, for example, is e i Class j samples of (1), for all e i ,e j ∈ε,i≠j,c can receive the message sent by epsilon but cannot access the sample set of epsilon, epsilon can receive the message sent by c but can not intercommunicate inside epsilon, and a federal small sample picture classifier is set to train and participate in the client setExternal client set
4. The method for classifying light-weight industrial pictures based on federal small sample learning as claimed in claim 1, wherein the specific operations for modeling the small sample picture classification task T are as follows: c-way K-shot picture classification task on classifier fIncluding support setsAnd query set The sample containing C x K labeled samples,containing C × K' samples to be classified (, requirement f is based onTo pairAnd (6) classifying.
5. The method of claim 1, wherein the small sample classifier f based on the lightweight relational network is constructed by using a relational network technology.
6. The method for classifying a lightweight industrial picture based on federal small sample learning as claimed in claim 5, wherein the specific operation is: constructing a multilayer CNN model f by an embedded module l θ And relation module g φ Composition, where θ, φ are parameters of functions l, g, l θ Extracting a sample feature map of an input image, wherein the sample feature map comprises two convolution blocks, each convolution block comprises 32 convolution layers with 3 multiplied by 3 filters, a batch normalization layer, a ReLU nonlinear layer and a 2 multiplied by 2 maximum pooling layer; g φ Determining whether two input samples belong to the same class based on the similarity of the feature maps, including the aboveTwo volume blocks, an 8-dimensional full-connected layer based on ReLU and a 1-dimensional full-connected layer based on Sigmoid, and for single-channel and three-channel images, the output size H of the last maximum pooling layer is 32 and 32 multiplied by 3.
7. The lightweight industrial picture classification method based on federal small sample learning as claimed in claim 1, wherein the training method of the small sample classifier f comprises the following specific operations: small sample picture classification task based onFor any sample pairObtaining a feature map l based on an embedding module θ (x a )、l θ (x b ) Computing a relationship score based on the relationship moduleWherein,for feature graph join operations, relationship scores for all sample pairs are organized using a relationship matrix, with classifier lossWherein:
updating model parameters theta by random gradient descent * ,φ * 。
8. The method of claim 1, wherein a side cloud collaborative deployment and training method of a small sample picture classifier based on a lightweight relational network is constructed by using a side cloud collaborative intelligence technology.
9. The method for classifying a lightweight industrial picture based on federal small sample learning as claimed in claim 8, comprising the steps of:
step 5.1, setting the current iteration times t ← 0, and initializing a cloud modelWherein theta is init 、φ init Initializing each participating client for random initialization parametersModel parameters ofEach participating client e i ∈ε i Organizing local task setsUpdating the current iteration time t ← 1;
step 5.2, each participating client e i ∈ε i FromRandom extraction taskObtaining all sample pairsCharacteristic diagram ofUploading all feature maps and corresponding sample labels to c;
step 5.3, for each participating client e i ∈ε i Based on step 4, c calculationUpdatingWhere α is the random gradient descent learning rate, given to each participating client e i ∈ε i Return theta g (t+1);
Step 5.5, updating the current iteration time T ← T +1, and if T is less than or equal to T, repeating the steps from 5.2 to 5.4;
10. Light industry picture classification system based on federal small sample study, its characterized in that includes:
the system modeling module is used for modeling an industrial picture classification system based on a federal small sample learning framework, and comprises a cloud server c and a group of industrial institutions epsilon with the same interest, and each e i E epsilon holds respective local sample sets separately
A small sample picture classification task modeling module used for classifying small sample pictures for each industrial mechanism in the constructed system modelModeling is performed, each task comprising a support setAnd query set
A classifier building module for building a classifier from the embedded module θ And relation module g φ The small sample classifier f based on the lightweight relational network is formed, wherein theta and phi are parameters of functions l and g;
a training module for classifying tasks based on small sample picturesConstructing a training method of a small sample classifier f; constructing an edge cloud collaborative deployment and training method of the small sample classifier based on a small sample classifier training method, wherein the complete small sample image classifier based on a lightweight relational network is deployed on c, and an embedded module is deployed on all e i E, on epsilon, training the classifier based on the training method in the step 4 along a discrete time point T of 0,1, … and T, wherein T is the iteration number of a preset model;
the output reasoning module is used for deploying and training the constructed small sample classifier based on the edge cloud collaborative deployment and training method of the small sample classifierAfter training is completed, each external client is initializedModel (2)Model reasoning is performed locally.
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