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Despite their remarkable image generation capabilities, text-to-image diffusion models inadvertently learn inappropriate concepts from vast and unfiltered training data, which leads to various ethical and business risks.
Auto-encoding variational bayes
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Jonathan Ho and Tim Salimans · 2022
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Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Javier Rando, Daniel Paleka, David Lindner, Lennart Heim, and Florian Tramèr · 2022
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Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Selective amnesia: A continual learning approach to forgetting in deep generative models
Alvin Heng and Harold Soh · 2023
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Susung Hong, Gyuseong Lee, Wooseok Jang, and Seungryong Kim · 2023
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Towards safe self-distillation of internet-scale text-to-image diffusion models
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Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong, Dennis Wei, and Sijia Liu · 2024
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Yu-Lin Tsai, Chia-Yi Hsu, Chulin Xie, Chih-Hsun Lin, Jia-You Chen, Bo Li, Pin-Yu Chen, Chia-Mu Yu, and Chun-Ying Huang · 2024
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Forget-me-not: Learning to forget in text-to-image diffusion models
Gong Zhang, Kai Wang, Xingqian Xu, Zhangyang Wang, and Humphrey Shi · 2024
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