Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models

• URL: http://arxiv.org/abs/2305.10120v2
• Date: Tue, 17 Oct 2023 01:59:27 GMT
• Title: Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models
• Authors: Alvin Heng, Harold Soh
• Abstract summary: We derive a technique inspired by continual learning to selectively forget concepts in pretrained deep generative models. Our method, dubbed Selective Amnesia, enables controllable forgetting where a user can specify how a concept should be forgotten.
• Score: 12.188240438657512
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent proliferation of large-scale text-to-image models has led to growing concerns that such models may be misused to generate harmful, misleading, and inappropriate content. Motivated by this issue, we derive a technique inspired by continual learning to selectively forget concepts in pretrained deep generative models. Our method, dubbed Selective Amnesia, enables controllable forgetting where a user can specify how a concept should be forgotten. Selective Amnesia can be applied to conditional variational likelihood models, which encompass a variety of popular deep generative frameworks, including variational autoencoders and large-scale text-to-image diffusion models. Experiments across different models demonstrate that our approach induces forgetting on a variety of concepts, from entire classes in standard datasets to celebrity and nudity prompts in text-to-image models. Our code is publicly available at https://github.com/clear-nus/selective-amnesia.

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