Content-aware domain adaptation for cross-domain classification

An adaptation system comprising:

• memory which stores:
• a learned transformation;
• a first classifier that has been trained on projected representations of objects from a first domain and respective labels, the projected representations having been generated by projecting original representations of the objects in the first domain with the learned transformation;
• optionally, a representation generator which generates original representations of unlabeled objects in a second domain;
• a transformation component which projects the original representations of the unlabeled objects with the learned transformation;
• a prediction component which predicts pseudo-labels for unlabeled objects in a second domain with the first classifier based on the projected representations of the unlabeled objects;
• an ensemble learning component which iteratively learns a classifier ensemble comprising a weighted combination of the first classifier and a second classifier, the learning including:
• training the second classifier on the original representations of the unlabeled objects for which a confidence for the respective pseudo-labels exceeds a threshold confidence;
• constructing a classifier ensemble as a weighted combination of the first classifier and the second classifier;
• predicting pseudo-labels for remaining unlabeled objects with the classifier ensemble based on their original representations;
• adjusting weights of the first and second classifiers in the classifier ensemble as a function of a learning rate;
• and
• repeating the training, constructing, predicting, and adjusting;
• and
• a processor which implements the transformation component, prediction component, and ensemble learning component.

An adaptation method comprising:

• learning a transformation based on features extracted from objects in first and second domains;
• computing a similarity between the first and second domains;
• projecting original representations of labeled objects in the first domain and unlabeled objects in the second domain with the learned projection;
• training a first classifier on the projected representations of the objects from the first domain and respective labels;
• predicting pseudo-labels for the projected representations of the unlabeled objects with the first classifier;
• iteratively learning a classifier ensemble comprising a weighted combination of the first classifier and a second classifier, the learning including:
• training the second classifier on the original representations of those of the unlabeled objects and respective pseudo-labels for which a confidence for the respective pseudo-labels exceeds a threshold confidence;
• constructing a classifier ensemble as a weighted combination of the first classifier and the second classifier;
• predicting pseudo-labels for the original representations of remaining unlabeled objects with the classifier ensemble;
• adjusting weights of the first and second classifiers in the classifier ensemble as a function of the computed similarity;
• and
• repeating the training, constructing, predicting, and adjusting,
• wherein at least one of the learning of the transformation, computing of the similarity, projecting of the original representations, training of the first classifier, predicting of the pseudo-labels, and iteratively learning the classifier ensemble is performed with a processor.