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

Alvin Heng, Harold Soh

Keywords: generative models, forgetting

TL;DR: We devise a method inspired by continual learning for selective forgetting of concepts in deep generative models.

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.

Submission Number: 4329