Zero-Shot Class Unlearning in CLIP with Synthetic Samples

Understand

Machine unlearning is a crucial area of research.

• It is driven by the need to remove sensitive information from models to safeguard individuals' right to be forgotten under rigorous regulations such as GDPR.
• In this work, we focus on unlearning within CLIP, a dual vision-language encoder model trained on a massive dataset of image-text pairs using contrastive loss.
• To achieve forgetting we expand the application of Lipschitz regularization to the multimodal context of CLIP.