This paper proposes an importance weighted adversarial nets-based method for unsupervised domain adaptation, specific for partial domain adaptation where the target domain has less number of classes compared to the source domain. Previous domain adaptation methods generally assume the identical label spaces, such that reducing the distribution divergence leads to feasible knowledge transfer. However, such an assumption is no longer valid in a more realistic scenario that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of classes. This paper extends the adversarial nets-based domain adaptation and proposes a novel adversarial nets-based partial domain adaptation method to identify the source samples that are potentially from the outlier classes and, at the same time, reduce the shift of shared classes between domains.
@cite_6: Adversarial learning has been successfully embedded into deep networks to learn transferable features, which reduce distribution discrepancy between the source and target domains. Existing domain adversarial networks assume fully shared label space across domains. In the presence of big data, there is strong motivation of transferring both classification and representation models from existing big domains to unknown small domains. This paper introduces partial transfer learning, which relaxes the shared label space assumption to that the target label space is only a subspace of the source label space. Previous methods typically match the whole source domain to the target domain, which are prone to negative transfer for the partial transfer problem. We present Selective Adversarial Network (SAN), which simultaneously circumvents negative transfer by selecting out the outlier source classes and promotes positive transfer by maximally matching the data distributions in the shared label space. Experiments demonstrate that our models exceed state-of-the-art results for partial transfer learning tasks on several benchmark datasets.
A recent report by Cao al @cite_6 also addresses the problem of transferring from big source domain to the target domain with a subset of classes. SAN trains a separate domain classifier for each class and introduces both instance-level and class-level weights according to the class probabilities given by label predictor. There are fundamental differences between the proposed method and the methods in @cite_6 . Firstly, their method uses a shared feature extractor for both domains. Secondly, our method only requires two domain classifiers rather than multiple domain classifiers (one per source class) which makes their method hardly scalable to a source data with a large number of classes and is computationally expensive. Lastly, our method does not require class level weight and hence be able to deal with imbalanced target data because if a class level weight is applied, the target classes with a smaller number of samples may not be able to be classified well after adaptation.