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 However, such an assumption is no longer valid in a more realistic scenario that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of classes.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.
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.
@cite_6
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Numerous algorithms have been proposed for transferring knowledge from a label-rich domain (source) to a label-scarce domain (target). Most of them are proposed for closed-set scenario, where the source and the target domain completely share the class of their samples. However, in practice, a target domain can contain samples of classes that are not shared by the source domain. We call such classes the “unknown class” and algorithms that work well in the open set situation are very practical. However, most existing distribution matching methods for domain adaptation do not work well in this setting because unknown target samples should not be aligned with the source. In this paper, we propose a method for an open set domain adaptation scenario, which utilizes adversarial training. This approach allows to extract features that separate unknown target from known target samples. During training, we assign two options to the feature generator: aligning target samples with source known ones or rejecting them as unknown target ones. Our method was extensively evaluated and outperformed other methods with a large margin in most settings.