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Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

期刊

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
卷 14, 期 2, 页码 68-81

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MCI.2019.2901088

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资金

  1. EU's Horizon 2020 Programme through the Innovation Action [645094]
  2. EU's Horizon 2020/EFPIA Innovative Medicines Initiative [115902]
  3. UK's Economic & Social Research Council [HJ-253479]
  4. ESRC [ES/R00398X/1] Funding Source: UKRI

向作者/读者索取更多资源

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.

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