Domain adaptation using probability distribution distance

Abstract

Methods and systems that train a neural network to classify inputs using a first set of labeled inputs corresponding to a source domain and adapt that neural network to classify inputs from another domain. The neural network includes a generator network and two or more classifier networks. The generator network is trained to receive inputs and generate features. The two or more classifier networks are trained to classify those features into classes to obtain class probability predictions. The neural network is adapted to a target domain, for example, by training the classifier networks to maximize a Wasserstein distance-based discrepancy between the class probability predictions of the classifier networks, by training the classifier networks to increase Wasserstein distance-based discrepancy or by training the generator network to minimize the Wasserstein distance-based discrepancy between the class probability predictions of the multiple classifier networks, or both.