System for domain adaptation with a domain-specific class means classifier

Patent metadata

Field	Value
Publication number	US-2016078359-A1
Application number	US-201414504837-A
Country	US
Kind code	A1
Filing date	Oct 2, 2014
Priority date	Sep 12, 2014
Publication date	Mar 17, 2016
Grant date	—

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.

First claim

What is claimed is:

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.

2 . The system of claim 1 , wherein each component is an exponentially decreasing function of the distance between the test sample and the domain-specific class representation.

3 . The system of claim 1 , wherein each domain-specific class representation is an average of the training samples of the respective domain that are labeled with the class.

4 . The system of claim 1 , wherein the distance between the test sample and each domain-specific class representation is computed in an embedding space into which the test sample and each domain-specific class representation is embedded with the same metric.

5 . The system of claim 1 , where the mixture components are Gaussian functions and the inverse of their covariance is shared and approximated by a low-rank matrix.

6 . The system of claim 1 , wherein the classifier models are learned by maximizing a sum of the log of the training sample class posteriors.

7 . The system of claim 1 , wherein the classifier model is of the form:

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or is a function thereof,
where p(c|x i ) represents the posterior probability of the class c for the test sample x i ;
w c represents the class specific mixture weight, that can be constant;
w d represents the mixture weight for a respective mixture component exp(−½d W (x i ,μ d c ), where d W (x i ,μ d c ) represents the distance between sample x i and the domain-specific class representation μ d c for a domain d selected from the target domain and the at least one source domain, and W represents an optional metric for embedding sample x i and each of the domain-specific class representations μ d c in a common embedding space; and
Z i is an optional normalizing factor.

8 . The system of claim 1 , wherein the training samples and test sample are multidimensional representations.

9 . The system of claim 7 , wherein the multidimensional representations are derived from images, videos, sounds, text or other multimedia documents.

10 . The system of claim 1 , wherein each of the source domain training samples is labeled with a label for one of the classes and fewer than all of the target domain training samples are labeled with a label for any of the classes.

11 . The system of claim 9 , wherein at least some of the target domain training samples are labeled with a label for at least one of the classes.

12 . The system of claim 10 , wherein the learning includes for each of a plurality of iterations,
performing at least one of:
adding to an active training set, which is derived from the source domain samples and labeled target domain samples, a most confident unlabeled target domain sample for each class, and
removing from the active training set a least confident source domain sample from each class; and
retraining a metric based on the active training set which is used to embed the test sample and a domain-specific class representation into an embedding space in which the distance is computed.

13 . The system of claim 1 , wherein the mixture weight for the target domain is higher than for each of the at least one source domains.

14 . The system o

Assignees

• Xerox Corp

Inventors

• Csurka Gabriela
• Chidlovskii Boris
• Perronnin Florent C

Classifications

• G06V10/776

  Validation; Performance evaluation · CPC title

• G06V10/761

  Proximity, similarity or dissimilarity measures · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N20/00 Primary

  Machine learning · CPC title