Certain and consistent domain adaptation

Bhadrinath Nagabandi, Andrew Dudley, Hemanth Venkateswara, Sethuraman Panchanathan

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


Unsupervised domain adaptation algorithms seek to transfer knowledge from labeled source datasets in order to predict the labels for target datasets in the presence of domain-shift. In this paper we propose the Certain and Consistent Domain Adaptation (CCDA) model for unsupervised domain adaptation. The CCDA aligns the source and target domains using adversarial training and reduces the domain adaptation problem to a semi supervised learning (SSL) problem. We estimate the target labels using consistency regularization and entropy minimization on the domain-aligned target samples whose predictions are consistent across multiple stochastic perturbations. We evaluate the CCDA on benchmark datasets and demonstrate that it outperforms competitive baselines from domain adaptation literature.

Original languageEnglish (US)
Title of host publicationSmart Multimedia - 2nd International Conference, ICSM 2019, Revised Selected Papers
EditorsTroy McDaniel, Stefano Berretti, Igor D.D. Curcio, Anup Basu
Number of pages16
ISBN (Print)9783030544065
StatePublished - 2020
Event2nd International Conference on Smart Multimedia, ICSM 2019 - San Diego, United States
Duration: Dec 16 2019Dec 18 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12015 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd International Conference on Smart Multimedia, ICSM 2019
Country/TerritoryUnited States
CitySan Diego


  • Consistency regularization
  • Domain adaptation
  • Entropy regularization
  • Semi supervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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