SYSTEM FOR DOMAIN ADAPTATION WITH A DOMAIN-SPECIFIC CLASS MEANS CLASSIFIER
First Claim
1. A classification system comprising:
- memory which stores;
for each of a set of classes, a classifier model for assigning a class probability to a test sample from a target domain, the classifier model having been learned with training samples from the target domain and training samples from at least one source domain different from the target domain, each classifier model modeling the respective class as a mixture of components, the mixture of components including a component for each of the at least one source domain and a component for the target domain, each component being a function of a distance between the test sample and a domain-specific class representation which is derived from the training samples of the respective domain that are labeled with the class, each of the components in the mixture being weighted by a respective mixture weight; and
instructions for labeling the test sample based on the class probabilities assigned by the classifier models; and
a processor in communication with the memory which executes the instructions.
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Accused Products
Abstract
A classification system includes memory which stores, for each of a set of classes, a classifier model for assigning a class probability to a test sample from a target domain. The classifier model has been learned with training samples from the target domain and from at least one source domain. Each classifier model models the respective class as a mixture of components, the component mixture including a component for each source domain and a component for the target domain. Each component is a function of a distance between the test sample and a domain-specific class representation which is derived from the training samples of the respective domain that are labeled with the class, each of the components in the mixture being weighted by a respective mixture weight. Instructions, implemented by a processor, are provided for labeling the test sample based on the class probabilities assigned by the classifier models.
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Citations
23 Claims
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1. A classification system comprising:
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memory which stores; for each of a set of classes, a classifier model for assigning a class probability to a test sample from a target domain, the classifier model having been learned with training samples from the target domain and training samples from at least one source domain different from the target domain, each classifier model modeling the respective class as a mixture of components, the mixture of components including a component for each of the at least one source domain and a component for the target domain, each component being a function of a distance between the test sample and a domain-specific class representation which is derived from the training samples of the respective domain that are labeled with the class, each of the components in the mixture being weighted by a respective mixture weight; and instructions for labeling the test sample based on the class probabilities assigned by the classifier models; and a processor in communication with the memory which executes the instructions. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A classifier learning method, comprising:
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for each of a set of domains including a target domain and at least one source domain, providing a set of samples, the source domain samples each being labeled with a class label for one of a set of classes, fewer than all of the target domain samples being labeled with any of the class labels; with a processor, learning a classifier model for each class with the target domain training samples and the training samples from the at least one source domain, each classifier model modeling the respective class as a mixture of components, the mixture of components including a component for each of the at least one source domain and a component for the target domain, each component being a function of a distance between the test sample and a domain-specific class representation which is derived from the training samples of the respective domain that are labeled with the class, each of the components in the mixture being weighted by a respective mixture weight. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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22. A method for learning a metric for a classifier model comprising:
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for each of a set of domains including a target domain and at least one source domain, providing a set of samples, the source domain samples each being labeled with a class label for one of a set of classes, fewer than all of the target domain samples being labeled with any of the class labels; composing an active training set from the labeled training samples; providing a metric for embedding samples in an embedding space; for each of a plurality of iterations, performing at least one of; a) adding to the active training set a most confident unlabeled target domain sample for each class, and b) removing from the active training set a least confident source domain sample from each class; and retraining the metric based on the active training set, the confidence used to remove and add samples being based on a classifier model that includes the trained metric, where each class is modeled as a mixture of components, and where there is one mixture component for each source domain and one for the target domain. - View Dependent Claims (23)
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Specification