WCAL: Weighted and center-aware adaptation learning for partial domain adaptation

Abstract

Partial domain adaptation, which aims to transfer knowledge from a source domain with rich labels to a unlabeled target domain where target class space is a subspace of source class space, is a challenging task in pattern recognition. Previous partial domain adaptation approaches tend to immerse in filtering out anomaly categories by weighting and the importance of ignoring the transferability of generated features. In the light of this, this article proposes a novel partial domain adaptation method, dubbed Weighted and Center-aware Adaptation Learning (WCAL). Specifically, WCAL presents a weighted adversarial learning module learns a category classifier to filter out the outlier categories from source domain. Also, it seeks a domain discriminator for cross-domain to further address the negative transfer. Then, the Center-aware adaptation learning module minimizes the distribution discrepancy across domains, which makes the features more transferability for the adaptation model. Extensive experiments on popular domain adaptation datasets testify that the proposed WCAL approach exceeds state-of-the-art baselines significantly with a large margin, in terms of average classification result, for example, 3.36% and 1.71% on Office-Home and Office-Caltech, respectively.