Erasing CLIP Memories: Non-Destructive, Data-Free Zero-Shot class Unlearning in CLIP Models

TL;DR

This paper presents a non-destructive, data-free method for unlearning specific classes in CLIP models by nullspace projection, effectively erasing class information without retraining or data access.

Contribution

The authors introduce a closed-form, nullspace projection technique for selective class unlearning in CLIP, avoiding retraining and data requirements, and enabling precise model decontamination.

Findings

Significant reduction in target class performance after unlearning

Method preserves overall model knowledge and multimodal capabilities

Partial projection balances unlearning effectiveness and knowledge retention

Abstract

We introduce a novel, closed-form approach for selective unlearning in multimodal models, specifically targeting pretrained models such as CLIP. Our method leverages nullspace projection to erase the target class information embedded in the final projection layer, without requiring any retraining or the use of images from the forget set. By computing an orthonormal basis for the subspace spanned by target text embeddings and projecting these directions, we dramatically reduce the alignment between image features and undesired classes. Unlike traditional unlearning techniques that rely on iterative fine-tuning and extensive data curation, our approach is both computationally efficient and surgically precise. This leads to a pronounced drop in zero-shot performance for the target classes while preserving the overall multimodal knowledge of the model. Our experiments demonstrate that even a partial projection can balance between complete unlearning and retaining useful information, addressing key challenges in model decontamination and privacy preservation.