Targeted Forgetting of Image Subgroups in CLIP Models

• URL: http://arxiv.org/abs/2506.03117v1
• Date: Tue, 03 Jun 2025 17:50:03 GMT
• Title: Targeted Forgetting of Image Subgroups in CLIP Models
• Authors: Zeliang Zhang, Gaowen Liu, Charles Fleming, Ramana Rao Kompella, Chenliang Xu,
• Abstract summary: Foundation models (FMs) such as CLIP have demonstrated impressive zero-shot performance across various tasks.<n>They often inherit harmful or unwanted knowledge from noisy internet-sourced datasets.<n>Existing model unlearning methods either rely on access to pre-trained datasets or focus on coarse-grained unlearning.<n>We propose a novel three-stage approach that progressively unlearns targeted knowledge while mitigating over-forgetting.
• Score: 30.78624907082701
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation models (FMs) such as CLIP have demonstrated impressive zero-shot performance across various tasks by leveraging large-scale, unsupervised pre-training. However, they often inherit harmful or unwanted knowledge from noisy internet-sourced datasets, compromising their reliability in real-world applications. Existing model unlearning methods either rely on access to pre-trained datasets or focus on coarse-grained unlearning (e.g., entire classes), leaving a critical gap for fine-grained unlearning. In this paper, we address the challenging scenario of selectively forgetting specific portions of knowledge within a class, without access to pre-trained data, while preserving the model's overall performance. We propose a novel three-stage approach that progressively unlearns targeted knowledge while mitigating over-forgetting. It consists of (1) a forgetting stage to fine-tune the CLIP on samples to be forgotten, (2) a reminding stage to restore performance on retained samples, and (3) a restoring stage to recover zero-shot capabilities using model souping. Additionally, we introduce knowledge distillation to handle the distribution disparity between forgetting, retaining samples, and unseen pre-trained data. Extensive experiments on CIFAR-10, ImageNet-1K, and style datasets demonstrate that our approach effectively unlearns specific subgroups while maintaining strong zero-shot performance on semantically similar subgroups and other categories, significantly outperforming baseline unlearning methods, which lose effectiveness under the CLIP unlearning setting.

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