Manifold discrimination partial adversarial domain adaptation

Abstract

Most traditional domain adaptations promote knowledge transfer with the assumption that source domain and target domain have the same label space. In a big data environment, we usually obtain the source dataset with a larger label space than the target dataset. Under this background, partial domain adaptation (PDA) comes into being naturally. In PDA, when the target domain label space is a subset of the source domain label space, cross-domain adaptation can be achieved by matching shared class samples in cross-domain. Many researchers make good progress in this field and propose different kinds of PDA methods. But in the existing PDA methods, the samples of the unknown class are still used for distribution alignment, and this makes the model suffer from the risk of negative transfer. In order to solve these problems, we propose manifold discrimination partial adversarial domain adaptation (MDPDA) to align the relevant classes across two domains and obtain effective domain adaptation. MDPDA combines manifold learning and adversarial learning. In MDPDA, we design the manifold discrimination module to map high-dimensional data into low-dimensional space and learn low-dimensional data information of source domain and target domain. We construct discriminative source domain feature space for model discrimination in MDPDA. MDPDA distinguishes and confuses the two domains through manifold learning and similarity structure, and generates discriminant feature space to promote the auxiliary network to better recognize the source domain samples. The comprehensive experiments on several benchmark datasets evaluate the superior performance of the proposed method compared to the state-of-the-art methods.