Zero-Shot Class Unlearning in CLIP with Synthetic Samples

TL;DR

This paper introduces a method for zero-shot class unlearning in CLIP that uses synthetic samples and Lipschitz regularization to effectively forget specific classes without requiring real data, ensuring privacy compliance.

Contribution

The work extends Lipschitz regularization to multimodal CLIP models and develops a synthetic sample generation approach for class unlearning without real forgetting data.

Findings

Effective class unlearning demonstrated on standard datasets.

Synthetic samples enable forgetting without real data.

Selective layer updates improve unlearning precision.

Abstract

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. Specifically, we ensure the smoothing of both visual and textual embeddings associated with the class intended to be forgotten relative to the perturbation introduced to the samples from that class. Additionally, importantly, we remove the necessity for real forgetting data by generating synthetic samples through gradient ascent maximizing the target class. Our forgetting procedure is iterative, where we track accuracy on a synthetic forget set and stop when accuracy falls below a chosen threshold. We employ a selective layers update strategy based on their average absolute gradient value to mitigate over-forgetting. We validate our approach on several standard datasets and provide thorough ablation analysis and comparisons with previous work.