Adapted domain specific class means classifier
First Claim
1. A domain-adapted classification method comprising:
- mapping an input set of representations to generate an output set of representations using a learned transformation, the input set of representations including a set of target samples from a target domain and, for each of a plurality of source domains, a class representation for each of a plurality of classes, the class representations each being representative of a set of source samples from the respective source domain labeled with a respective class, the output set of representations including an adapted representation of each of the target samples and an adapted class representation for each of the classes for each of the source domains;
predicting a class label for at least one of the target samples based on the output set of representations; and
outputting information based on the predicted class label,wherein the mapping comprises, for at least one iteration;
learning a transformation that minimizes a reconstruction error when a corrupted set of representations, generated from the input set of representations, is transformed, with the transformation, to generate a reconstructed set of representations, andoutputting the reconstructed set of representations or adapted representations generated therefrom,wherein each of the class representations and the target samples is a multidimensional representation comprising at least 10 dimensions, andwherein at least one of the mapping of the input set of representations and the predicting of the class label is performed with a processor.
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Abstract
A domain-adapted classification system and method are disclosed. The method includes mapping an input set of representations to generate an output set of representations, using a learned transformation. The input set of representations includes a set of target samples from a target domain. The input set also includes, for each of a plurality of source domains, a class representation for each of a plurality of classes. The class representations are representative of a respective set of source samples from the respective source domain labeled with a respective class. The output set of representations includes an adapted representation of each of the target samples and an adapted class representation for each of the classes for each of the source domains. A class label is predicted for at least one of the target samples based on the output set of representations and information based on the predicted class label is output.
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Citations
22 Claims
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1. A domain-adapted classification method comprising:
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mapping an input set of representations to generate an output set of representations using a learned transformation, the input set of representations including a set of target samples from a target domain and, for each of a plurality of source domains, a class representation for each of a plurality of classes, the class representations each being representative of a set of source samples from the respective source domain labeled with a respective class, the output set of representations including an adapted representation of each of the target samples and an adapted class representation for each of the classes for each of the source domains; predicting a class label for at least one of the target samples based on the output set of representations; and outputting information based on the predicted class label, wherein the mapping comprises, for at least one iteration; learning a transformation that minimizes a reconstruction error when a corrupted set of representations, generated from the input set of representations, is transformed, with the transformation, to generate a reconstructed set of representations, and outputting the reconstructed set of representations or adapted representations generated therefrom, wherein each of the class representations and the target samples is a multidimensional representation comprising at least 10 dimensions, and wherein at least one of the mapping of the input set of representations and the predicting of the class label is performed with a processor. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A classification system comprising:
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a mapping component which maps an input set of representations to generate an output set of representations, the input set of representations including a set of target samples from a target domain and, for each of a plurality of source domains, a class representation for each of a plurality of classes, the class representations each being representative of a set of source samples labeled with a respective class, the output set of representations including an adapted representation of each of the target samples and an adapted class representation for each of the classes for each of the source domains; a classifier component which, for each of the classes, generates a classifier based on the adapted class representations for that class for each of the source domains and predicts a label for at least one of the target samples using the classifiers; an output component which outputs information based on the predicted class label; and a processor which implements the combining component, mapping component, classifier component, and output component. - View Dependent Claims (19, 21, 22)
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20. A classification method comprising:
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with a stacked marginalized Denoising Autoencoder, learning a transformation for mapping an input set of representations to generate an output set of representations, the input set of representations including a set of target samples from a target domain and, for each of a plurality of source domains, a class representation for each of a plurality of classes, the class representations each being representative of a respective set of source samples from a respective one of the source domains labeled with a respective class, the output set of representations including an adapted representation of each of the target samples and an adapted class representation for each of the classes for each of the source domains; mapping the input set of representations with the learned transformation; for each class, generating a classifier with the adapted class representations for that class for each of the source domains; predicting a class label for at least one of the target samples with the classifiers; and outputting information based on the predicted class label, wherein the learning, the mapping and the predicting is performed with a processor.
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Specification