Multi-Modal Generative Adversarial Networks Make Realistic and Diverse but Untrustworthy Predictions When Applied to Ill-posed Problems

John Hyatt, Michael Lee. Multi-Modal Generative Adversarial Networks Make Realistic and Diverse but Untrustworthy Predictions When Applied to Ill-posed Problems. In Huáscar Espinoza, John McDermid, Xiaowei Huang 0001, Mauricio Castillo-Effen, Xin Cynthia Chen, José Hernández-Orallo, Seán Ó hÉigeartaigh, Richard Mallah, editors, Proceedings of the Workshop on Artificial Intelligence Safety 2021 (SafeAI 2021) co-located with the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021), Virtual, February 8, 2021. Volume 2808 of CEUR Workshop Proceedings, CEUR-WS.org, 2021. [doi]

Abstract

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