Transductive adaptation of classifiers without source data
First Claim
1. A classification method comprising:
- providing access to a pretrained classifier which has been trained on source samples in a source domain and their respective class labels,thereafter, receiving a collection of unlabeled target samples for a target domain, different from the source domain, each target sample comprising a multidimensional feature representation;
with the pretrained classifier, generating a class label prediction for each of the target samples in the collection;
for at least one iteration, without access to source samples from the source domain,augmenting each target sample multidimensional feature representation with a respective class label prediction output by the pretrained classifier to form an augmented representation,generating a set of corrupted target samples from the augmented representations, andlearning a transformation that minimizes a reconstruction error for the set of corrupted target samples;
generating an adapted class label prediction for at least one of the target samples in the collection using the learned transformation; and
outputting information based on the adapted class label prediction,wherein the augmenting, generating a set of corrupted samples, learning a transformation, and generating an adapted class label prediction are performed with a hardware processor.
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Abstract
A classification method includes receiving a collection of samples, each sample comprising a multidimensional feature representation. A class label prediction for each sample in the collection is generated with one or more pretrained classifiers. For at least one iteration, each multidimensional feature representation is augmented with a respective class label prediction to form an augmented representation, a set of corrupted samples is generated from the augmented representations, and a transformation that minimizes a reconstruction error for the set of corrupted samples is learned. An adapted class label prediction for at least one of the samples in the collection is generated using the learned transformation and information is output, based on the adapted class label prediction. The method is useful in predicting labels for target samples where there is no access to source domain samples that are used to train the classifier and no access to target domain training data.
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Citations
20 Claims
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1. A classification method comprising:
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providing access to a pretrained classifier which has been trained on source samples in a source domain and their respective class labels, thereafter, receiving a collection of unlabeled target samples for a target domain, different from the source domain, each target sample comprising a multidimensional feature representation; with the pretrained classifier, generating a class label prediction for each of the target samples in the collection; for at least one iteration, without access to source samples from the source domain, augmenting each target sample multidimensional feature representation with a respective class label prediction output by the pretrained classifier to form an augmented representation, generating a set of corrupted target samples from the augmented representations, and learning a transformation that minimizes a reconstruction error for the set of corrupted target samples; generating an adapted class label prediction for at least one of the target samples in the collection using the learned transformation; and outputting information based on the adapted class label prediction, wherein the augmenting, generating a set of corrupted samples, learning a transformation, and generating an adapted class label prediction are performed with a hardware processor. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A classification system comprising:
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a first prediction component which uses a pretrained classifier to generate a class label prediction for each sample in a collection of unlabeled samples, each sample comprising a multidimensional feature representation; a learning component which learns a transformation, the learning component including a stack of autoencoders, each of the autoencoders including an encoder which corrupts input feature vectors and a decoder which reconstructs the input feature vectors from the corrupted feature vectors, the transformation being learned to minimize the reconstruction error, wherein in a first of the layers, the input feature vectors include the multidimensional feature representations augmented by their class label predictions output by the pretrained classifier and in a second of the layers, the input feature vectors are based on class label predictions output by the first layer; a second prediction component which generates an adapted class label prediction for at least one of the samples in the collection using the learned transformation; an output component which outputs information based on the adapted class label prediction; and a hardware processor which implements the components. - View Dependent Claims (18)
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19. A classification method comprising:
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receiving a collection of target samples in a target domain, each sample comprising a multidimensional feature representation; with a pretrained classifier trained on labeled source samples in a source domain, generating a class label prediction for each of the target samples in the collection; with a hardware processor, in a first of a plurality of iterations, augmenting each multidimensional feature representation with a respective one of the class label predictions generated by the pretrained classifier to form an augmented representation, generating a set of corrupted samples from the augmented representations, learning a transformation that minimizes a reconstruction error for the set of corrupted samples, and generating an adapted class label prediction for each of the target samples in the collection using the learned transformation; in at least a second of the plurality of iterations, repeating the generating of a set of corrupted samples, learning a transformation, and generating adapted class label predictions, wherein the set of corrupted samples are generated from augmented representations that are based on adapted class label predictions from a preceding iteration; and outputting information based on the adapted class label predictions of one of the plurality of iterations. - View Dependent Claims (20)
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Specification