Reinforced Reweighting for Self-Supervised Partial Domain Adaptation

Domain adaptation enables the reduction of distribution differences across domains, allowing for effective knowledge transfer from one domain to a different domain. In recent years, partial domain adaptation (PDA) has attracted growing interest due to its focus on a more realistic scenario, where the target label space is a subset of the source label space. As the source and target domains do not possess the same label space in the PDA setting, it is challenging but crucial to mitigate the domain gap without incurring negative transfer. In this article, we propose a reinforced reweighting united with self-supervised adaptation (R2SA) method to address the challenges in PDA by leveraging the merits of deep reinforcement learning (DRL) and self-supervised learning (SSL) simultaneously in a cooperative way. Reinforced reweighting aims to learn a source reweighting policy automatically based on information provided by the PDA model, while self-supervised adaptation aims to boost the adaptability of the PDA model through an additional self-supervised objective on the target domain. Extensive experiments on several cross-domain benchmarks demonstrate that our method achieves state-of-the-art results, with larger performance gains on more challenging tasks.