[2603.00992] Compensation-free Machine Unlearning in Text-to-Image Diffusion Models by Eliminating the Mutual Information

Computer Science > Machine Learning arXiv:2603.00992 (cs) [Submitted on 1 Mar 2026] Title:Compensation-free Machine Unlearning in Text-to-Image Diffusion Models by Eliminating the Mutual Information Authors:Xinwen Cheng, Jingyuan Zhang, Zhehao Huang, Yingwen Wu, Xiaolin Huang View a PDF of the paper titled Compensation-free Machine Unlearning in Text-to-Image Diffusion Models by Eliminating the Mutual Information, by Xinwen Cheng and 4 other authors View PDF HTML (experimental) Abstract:The powerful generative capabilities of diffusion models have raised growing privacy and safety concerns regarding generating sensitive or undesired content. In response, machine unlearning (MU) -- commonly referred to as concept erasure (CE) in diffusion models -- has been introduced to remove specific knowledge from model parameters meanwhile preserving innocent knowledge. Despite recent advancements, existing unlearning methods often suffer from excessive and indiscriminate removal, which leads to substantial degradation in the quality of innocent generations. To preserve model utility, prior works rely on compensation, i.e., re-assimilating a subset of the remaining data or explicitly constraining the divergence from the pre-trained model on remaining concepts. However, we reveal that generations beyond the compensation scope still suffer, suggesting such post-remedial compensations are inherently insufficient for preserving the general utility of large-scale generative models. Therefor...