CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP

Machine unlearning (MU) has gained significant attention as a means to remove the influence of specific data from a trained model without requiring full retraining. While progress has been made in unimodal domains like text and image classification, unlearning in multimodal models remains relatively under-explored. In this work, we address the unique challenges of unlearning in CLIP, a prominent multimodal model that aligns visual and textual representations. We introduce CLIPErase, a novel approach that disentangles and selectively forgets both visual and textual associations, ensuring that unlearning does not compromise model performance. CLIPErase consists of three key modules: a Forgetting Module that disrupts the associations in the forget set, a Retention Module that preserves performance on the retain set, and a Consistency Module that maintains consistency with the original model. Extensive experiments on CIFAR-100, Flickr30K, and Conceptual 12M across five CLIP downstream tasks, as well as an evaluation on diffusion models, demonstrate that CLIPErase effectively removes designated associations from multimodal samples in downstream tasks, while preserving the model's performance on the retain set after unlearning. The project's code is available at: https://tianyuyang-anna.github.io/ClipErase-ACL/.