Discovering Shared Space for Visual Recognition by Cross-Domain Residual Learning

Abstract In order to solve the problem of inconsistent data distribution in machine learning, domain adaptation based on feature representation methods extract features from source domain, and transfer to target domain for classi cation. The existing feature representation based methods mainly solve the problem of inconsistent feature distribution between the source domain data and the target domain data, but only few methods analyze the correlation of cross-domain features between original space and shared latent space, which reduce the performance of domain adaptation. To this end, we propose a domain adaptation method with residual module, the main ideas of which are: (1) transfer the source domain data features to the target domain data through the shared latent space to achieve features sharing; (2) build a cross domain residual learning model using the latent feature space as the residual connection of the original feature space, which improves the propagation e ciency of features; (3) regular feature space to sparse features representation, which can improve the robustness of the model; and (4) give optimization algorithm, and the experiments on the public visual datasets verify the e ectiveness of the method.