Zero-Shot Class Unlearning in CLIP with Synthetic Samples
This work addresses the need for machine unlearning in multimodal models to protect privacy, though it is incremental as it builds on existing Lipschitz regularization techniques.
The paper tackles the problem of removing sensitive information from CLIP models to comply with regulations like GDPR by introducing a zero-shot class unlearning method that uses synthetic samples and Lipschitz regularization, achieving effective forgetting as validated on standard datasets.
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.