Adaptive Graph Adversarial Networks for Partial Domain Adaptation
Youngeun Kim, Sungeun Hong
Abstract
This article tackles Partial Domain Adaptation (PDA) where the target label set is a subset of the source label set. A key challenging issue in PDA is to prevent negative transfer by isolating source-private classes. Since there is no label information for a target domain, PDA methods require to estimate a label commonness score between source and target domains. Existing approaches use either class-level or sample-level commonness to alleviate the negative transfer issue. However, class-level methods assign the same label commonness to all samples of the same class without considering each sample’s characteristics. Also, the recently introduced sample-level approaches show better performance but they still suffer from negative transfer due to non-trivial anomaly samples. To address these limitations, we propose Adaptive Graph Adversarial Networks (AGAN) consisting of two specialized modules. The adaptive class-relational graph module is designed to utilize the intra- and inter-domain structures through adaptive feature propagation. Complementarily, the sample-level commonness predictor computes a commonness score of each sample. Extensive experimental results on public PDA benchmark datasets demonstrate that our structure-aware method outperforms state-of-the-art methods.