Li, Liu, Lin, Wen, Su, Huang, Ding (2020) Deep Residual Correction Network for Partial Domain Adaptation IEEE transactions on pattern analysis and machine intelligence ()
Deep domain adaptation methods have achieved appealing performance by learning transferable representations from a well-labeled source domain to a different but related unlabeled target domain. Most existing works assume source and target data share the identical label space, which is often difficult to be satisfied in many real-world applications. There is a more practical scenario called partial domain adaptation, where the target label space is a subset of the source label space. In this case, reinforcing the positive effects of the most relevant source subclasses and reducing the negative impacts of irrelevant source subclasses are crucial. This paper proposes an efficiently-implemented Deep Residual Correction Network by plugging one residual block into the source network, which effectively enhances the adaptation from source to target and explicitly weakens the influence from the irrelevant source classes. Moreover, we design a weighted class-wise domain alignment loss to couple two domains by matching the feature distributions of shared classes between source and target. Comprehensive experiments on partial, traditional and fine-grained cross-domain visual recognition demonstrate that DRCN is superior to the competitive deep domain adaptation approaches.