Visual Domain Adaptation: A survey of recent advances

Vishal M. Patel, Raghuraman Gopalan, Ruonan Li, Rama Chellappa

Research output: Contribution to journalArticlepeer-review

452 Scopus citations

Abstract

In pattern recognition and computer vision, one is often faced with scenarios where the training data used to learn a model have different distribution from the data on which the model is applied. Regardless of the cause, any distributional change that occurs after learning a classifier can degrade its performance at test time. Domain adaptation tries to mitigate this degradation. In this article, we provide a survey of domain adaptation methods for visual recognition. We discuss the merits and drawbacks of existing domain adaptation approaches and identify promising avenues for research in this rapidly evolving field.

Original languageEnglish (US)
Article number7078994
Pages (from-to)53-69
Number of pages17
JournalIEEE Signal Processing Magazine
Volume32
Issue number3
DOIs
StatePublished - May 1 2015

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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