Partially Adversarial Learning and Adaptation

An image classification system for a specific target domain is usually trained with initialization from a source domain given with a large number of classes, particularly in an application of image recognition. The classes in target domain are usually seen as a subset in source domain. Partial domain adaptation aims to tackle this generalization issue where no labeled data are provided in target domain. This paper presents an adversarial learning for partial domain adaptation where a symmetric metric based on the Wasserstein distance is adopted in an adversarial learning objective. We build a Wasserstein partial transfer network where the Wasserstein adversarial objective is jointly optimized to partially transfer the relevance knowledge from source to target domains. The geometric property for optimal transport is assured to mitigate the gradient vanishing problem in adversarial training. The neural network components for feature extraction, relevance transfer, domain matching and task classification are jointly trained by solving a minimax optimization over multiple objectives. Experiments on image classification show the merits of the proposed partially adversarial domain adaptation over different tasks.