Selective, Controlled and Domain-Agnostic Unlearning in Pretrained CLIP: A Training- and Data-Free Approach

• URL: http://arxiv.org/abs/2512.14113v1
• Date: Tue, 16 Dec 2025 05:54:13 GMT
• Title: Selective, Controlled and Domain-Agnostic Unlearning in Pretrained CLIP: A Training- and Data-Free Approach
• Authors: Ashish Mishra, Gyanaranjan Nayak, Tarun Kumar, Arpit Shah, Suparna Bhattacharya, Martin Foltin,
• Abstract summary: Real-world applications often demand the removal (or "unlearning") of specific object classes without requiring additional data or retraining.<n>We propose a novel training- and data-free unlearning framework that enables three distinct forgetting paradigms.<n>By leveraging a multimodal nullspace through synergistic integration of text prompts and synthesized visual prototypes, our method efficiently removes undesired class information while preserving the remaining knowledge.
• Score: 4.820351122363815
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Pretrained models like CLIP have demonstrated impressive zero-shot classification capabilities across diverse visual domains, spanning natural images, artistic renderings, and abstract representations. However, real-world applications often demand the removal (or "unlearning") of specific object classes without requiring additional data or retraining, or affecting the model's performance on unrelated tasks. In this paper, we propose a novel training- and data-free unlearning framework that enables three distinct forgetting paradigms: (1) global unlearning of selected objects across all domains, (2) domain-specific knowledge removal (e.g., eliminating sketch representations while preserving photo recognition), and (3) complete unlearning in selective domains. By leveraging a multimodal nullspace through synergistic integration of text prompts and synthesized visual prototypes derived from CLIP's joint embedding space, our method efficiently removes undesired class information while preserving the remaining knowledge. This approach overcomes the limitations of existing retraining-based methods and offers a flexible and computationally efficient solution for controlled model forgetting.

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