Domain adaptation of deep neural networks
AssigneeSap SE
InventorsErick David SANTILLÁN PEREZ, David KERNERT
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Abstract
Disclosed herein are system, method, and computer program product embodiments for adapting machine learning models for use in additional applications. For example, feature extraction models are readily available for use in applications such as image detection. These feature extraction models can be used to label inputs (such as images) in conjunction with other deep neural network models. However, in adapting the feature extraction models to these uses, it becomes problematic to improve the quality of their results on target data sets, as these feature extraction models are large and resistant to retraining. Approaches disclosed herein include a transfer layer for providing fast retraining of machine learning models.
Claims20
A computer implemented method, comprising:
- extracting, by one or more computing devices, features from an input by a feature extractor;
- processing, by the one or more computing devices, the features by transfer layers configured as a deep neural network to produce a transfer output;
- classifying, by the one or more computing devices, the input with a label by a label predictor implemented as a deep neural network, based on the transfer output;
- classifying, by the one or more computing devices, the input as either a labeled source input or an unlabeled target input by a domain classifier;
- and
- back propagating, by the one or more computing devices, a loss at the domain classifier to the domain classifier and the transfer layers.
A system, comprising:
- a memory configured to store operations;
- and
- one or more processors configured to perform the operations, the operations comprising:
- extracting features from an input by a feature extractor,
- processing the features by transfer layers configured as a deep neural network to produce a transfer output,
- classifying the input with a label by a label predictor implemented as a deep neural network, based on the transfer output,
- classifying the input as either a labeled source input or an unlabeled target input by a domain classifier, and
- back propagating a loss at the domain classifier to the domain classifier and the transfer layers.
A computer readable storage device having instructions stored thereon, execution of which, by one or more processing devices, causes the one or more processing devices to perform operations comprising:
- extracting features from an input by a feature extractor;
- processing the features by transfer layers configured as a deep neural network to produce a transfer output;
- classifying the input with a label by a label predictor implemented as a deep neural network, based on the transfer output;
- classifying the input as either a labeled source input or an unlabeled target input by a domain classifier;
- and
- back propagating a loss at the domain classifier to the domain classifier and the transfer layers.
Dependencies are inferred from the claim text.