Looking back at Labels: A Class based Domain Adaptation Technique
Abstract
In this paper, we tackle a problem of Domain Adaptation. In a domain adaptation setting, there is provided a labeled set of examples in a source dataset with multiple classes being present and a target dataset that has 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 structured adapted space. Using this formulation, we obtain the 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.
Code Released!!
tSNE Plot Before Adaptation
tSNE Plot After Adaptation
BibTex
@InProceedings{kurmi2019looking,author = {Kurmi, Vinod Kumar and Namboodiri, Vinay P},
title = {Looking back at Labels: A Class based Domain Adaptation Technique},
booktitle = {International Joint Conference on Neural Networks (IJCNN) },
month = {July},
year = {2019}
}
Acknowledgement
We acknowledge the help provided by Delta Lab members, who have supported us for this research activity.