Attending to Discriminative Certainty for Domain Adaptation

Vinod Kumar Kurmi, Shanu Kumar, Vinay P Namboodiri

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

89 Citations (SciVal)
52 Downloads (Pure)


In this paper, we aim to solve for unsupervised domain adaptation of classifiers where we have access to label information for the source domain while these are not available for a target domain. While various methods have been proposed for solving these including adversarial discriminator based methods, most approaches have focused on the entire image based domain adaptation. In an image, there would be regions that can be adapted better, for instance, the foreground object may be similar in nature. To obtain such regions, we propose methods that consider the probabilistic certainty estimate of various regions and specify focus on these during classification for adaptation. We observe that just by incorporating the probabilistic certainty of the discriminator while training the classifier, we are able to obtain state of the art results on various datasets as compared against all the recent methods. We provide a thorough empirical analysis of the method by providing ablation analysis, statistical significance test, and visualization of the attention maps and t-SNE embeddings. These evaluations convincingly demonstrate the effectiveness of the proposed approach.
Original languageEnglish
Title of host publicationIEEE Conference on Computer Vision and Pattern Recognition
Place of PublicationNew York, New York
Number of pages10
ISBN (Electronic)9781728132938
ISBN (Print)9781728132945
Publication statusPublished - 9 Jan 2020
Event2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) - Long Beach, USA United States
Duration: 16 Jun 201920 Jun 2019

Publication series

Name2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075


Conference2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Country/TerritoryUSA United States
CityLong Beach

Bibliographical note

CVPR 2019 Accepted, Project:


  • cs.CV
  • cs.LG
  • stat.ML


Dive into the research topics of 'Attending to Discriminative Certainty for Domain Adaptation'. Together they form a unique fingerprint.

Cite this