Looking back at Labels: A Class based Domain Adaptation Technique
This work addresses domain adaptation for classifiers, which is an incremental improvement over existing adversarial methods by incorporating class information.
The paper tackles domain adaptation for multi-class classification by proposing an informed adversarial discriminator that leverages class structure from the source dataset to guide feature transformation in the target domain, achieving state-of-the-art results on benchmark datasets.
In this paper, we solve the problem of adapting classifiers across domains. We consider the problem of domain adaptation for multi-class classification where we are provided a labeled set of examples in a source dataset and we are provided a target dataset with no supervision. In this setting, we propose an adversarial discriminator based approach. While the approach based on adversarial discriminator has been previously proposed; in this paper, we present an informed adversarial discriminator. Our observation relies on the analysis that shows that if the discriminator has access to all the information available including the class structure present in the source dataset, then it can guide the transformation of features of the target set of classes to a more structure adapted space. Using this formulation, we obtain state-of-the-art results for the standard evaluation on benchmark datasets. We further provide detailed analysis which shows that using all the labeled information results in an improved domain adaptation.