Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives [Review Article]

Jing Han, Zixing Zhang, Nicholas Cummins, Björn Schuller

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities.

Original languageEnglish
Article number8688367
Pages (from-to)68-81
Number of pages14
JournalIEEE Computational Intelligence Magazine
Volume14
Issue number2
DOIs
StatePublished - May 2019
Externally publishedYes

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