Looking back at Labels: A Class based Domain Adaptation Technique

Vinod Kumar Kurmi, Vinay P. Namboodiri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherIEEE
ISBN (Electronic)9781728119854
DOIs
Publication statusE-pub ahead of print - 30 Sep 2019
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: 14 Jul 201919 Jul 2019

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July
ISSN (Print)2161-4393
ISSN (Electronic)2161-4407

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
CountryHungary
CityBudapest
Period14/07/1919/07/19

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Kurmi, V. K., & Namboodiri, V. P. (2019). Looking back at Labels: A Class based Domain Adaptation Technique. In 2019 International Joint Conference on Neural Networks, IJCNN 2019 [8852199] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July). IEEE. https://doi.org/10.1109/IJCNN.2019.8852199