Domain Adversarial Reinforcement Learning for Partial Domain Adaptation
This addresses a practical problem in domain adaptation where label spaces aren't fully shared, though it appears to be an incremental improvement combining existing techniques.
The paper tackles partial domain adaptation where target domains have fewer classes than source domains, proposing a Domain Adversarial Reinforcement Learning (DARL) framework to automatically select relevant source instances and learn transferable features. Experiments on benchmark datasets show DARL achieves superior performance over existing state-of-the-art methods.
Partial domain adaptation aims to transfer knowledge from a label-rich source domain to a label-scarce target domain which relaxes the fully shared label space assumption across different domains. In this more general and practical scenario, a major challenge is how to select source instances in the shared classes across different domains for positive transfer. To address this issue, we propose a Domain Adversarial Reinforcement Learning (DARL) framework to automatically select source instances in the shared classes for circumventing negative transfer as well as to simultaneously learn transferable features between domains by reducing the domain shift. Specifically, in this framework, we employ deep Q-learning to learn policies for an agent to make selection decisions by approximating the action-value function. Moreover, domain adversarial learning is introduced to learn domain-invariant features for the selected source instances by the agent and the target instances, and also to determine rewards for the agent based on how relevant the selected source instances are to the target domain. Experiments on several benchmark datasets demonstrate that the superior performance of our DARL method over existing state of the arts for partial domain adaptation.