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

Understand

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.