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

A method for learning a metric for a classifier model comprising:

• 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.