CENet: Consolidation-and-Exploration Network for Continuous Domain Adaptation
Unsupervised Domain Adaptation (UDA) deals with transferring knowledge from labeled source domains to an unlabeled target domain under domain shift. However, this does not reflect the breadth of scenarios that arise in real-world applications since source domains could increase. A plausible conjecture is: can we train a lifelong learning model learned on continuous source domains given the target without the presence of labels? We formalize this task as the Continuous Domain Adaptation (CDA) and empirically show that conventional domain adaptation methods may suffer severe generalization deterioration due to the limited incremental transferability and negative transfer. To tackle this issue, we propose a novel sample-to-sample framework---Consolidation-and-Exploration Network (CENet) to facilitate incremental transferring. This method underscores both the qualitative and quantitative relationship between samples. Moreover, we conduct comprehensive experiments to evaluate the effectiveness of each component in our pair-based method. Extensive experiments show that our approach achieves significant improvement over related state-of-the-art methods. Our source code will be publicly available.