Zero-shot CLIP Class Forgetting via Text-image Space Adaptation

Efficient class forgetting has attracted significant interest due to the high computational cost of retraining models from scratch whenever classes need to be forgotten. This need arises from data privacy regulations, the necessity to remove outdated information, and the possibility to enhance model robustness and security.
In this paper we address class forgetting in vision-language CLIP model. Modern class forgetting methods for CLIP have demonstrated that zero-shot forgetting is achievable by generating synthetic data and fine-tuning both visual and textual encoders with a regularization loss. Our approach shows that class forgetting in CLIP can be accomplished in a zero-shot manner without any visual data by adapting the shared vision-text space of CLIP, thereby making the class forgetting process more efficient. Our method delivers superior results, demonstrating strong performance and complete class removal, regardless of the visual encoder used in CLIP. Furthermore, we explore what exactly is being targeted by the class forgetting algorithm discovering some interesting properties of CLIP features.