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US 12,585,933 B2Granted
Transfer learning with augmented neural networks
AssigneeInternational Business Machines Corporation
InventorsChungkuk Yoo, Bumsoo Kang, Minsik Cho
Life of this patent
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Abstract
A pretrained model is selected to operate in an augmented model configuration with a submodel. The submodel is trained using training data corresponding to a second domain, whereas the pretrained model is trained to operate on data of a first domain. The pretrained model is augmented, to form the augmented model configuration, with the submodel, by combining a first feature map being output from a layer in the pretrained model with a second feature map being output from a layer in the submodel. The combining forms a combined feature map. The combined feature map is input into a different layer in the submodel.
Claims20
A method comprising:
- selecting a pretrained neural network model to operate in an augmented neural network model configuration with a neural network submodel, wherein the pretrained neural network model is pretrained to classify a first type of object in a first domain;
- training, using a processor and a memory, the neural network submodel using training data corresponding to a second domain, wherein the training comprises training the neural network submodel to classify a second type of object in the second domain;
- and
- augmenting, to form the augmented neural network model configuration, the pretrained neural network model with the neural network submodel, the augmenting comprising:
- rearranging a subset of channels from an output of a layer in the pretrained neural network model, the subset including those channels whose channel selection parameters cause those channels to have a greater than a threshold weight, the rearranging further applying a first weight vector to the subset of channels according to a relevance criterion, the subset including the first channel as a highest weighted channel;
- combining, to form a combined feature map, a first feature map being output from a layer in the pretrained neural network model with a second feature map being output from a layer in the neural network submodel;
- and inputting the combined feature map into a different layer in the neural network submodel.
4 more elements
A method comprising:
- selecting a pretrained neural network model to operate in an augmented model configuration with a neural network submodel, wherein the pretrained neural network model is pretrained to classify a first type of object in a first domain;
- training, using a processor and a memory, the neural network submodel using training data corresponding to a second domain, wherein the training comprises training the neural network submodel to classify a second type of object in the second domain;
- and
- augmenting, to form the augmented model configuration, the pretrained neural network model with the neural network submodel, the augmenting comprising:
- rearranging a subset of channels from an output of a layer in the pretrained neural network model, the subset including those channels whose channel selection parameters cause those channels to have a greater than a threshold weight, the rearranging further applying a first weight vector to the subset of channels according to a relevance criterion, the subset including the first channel as a highest weighted channel;
- adjusting an attention value of a channel in a first feature map being output from a layer in the pretrained neural network model, wherein the adjusting causes a first feature matrix of the channel in the first feature map to have a greater weight relative to a second feature matrix of a different channel in the first feature map;
- combining, to form a combined feature map, a first feature matrix of the channel in the first feature map with a second feature map being output from a layer in the neural network submodel;
- and
- inputting the combined feature map into a different layer in the neural network submodel.
6 more elements
A method comprising:
- selecting a pretrained model to operate in an augmented model configuration with a submodel, wherein the pretrained model is pretrained to classify a first type of object in a first domain;
- training, using a processor and a memory, the submodel using training data corresponding to a second domain, wherein the training comprises training the submodel to classify a second type of object in the second domain;
- and
- augmenting, to form the augmented model configuration, the pretrained model with the submodel, the augmenting comprising:
- applying a channel selection parameter to a first channel in a first feature map being output from a layer in the pretrained model, wherein the applying causes a first feature matrix of the first channel in the first feature map to have a greater weight relative to a second feature matrix of a different channel in the first feature map;
- rearranging a subset of channels from the output of the layer in the pretrained model, the subset including those channels whose channel selection parameters cause those channels to have a greater than a threshold weight, the rearranging further applying a first weight vector to the subset of channels according to a relevance criterion, the subset including the first channel as a highest weighted channel;
- combining, to form a combined feature map, a first feature matrix of the first channel in the first feature map with a second feature map being output from a layer in the submodel;
- and
- inputting the combined feature map into a different layer in the submodel.
6 more elements
Dependencies are inferred from the claim text.