With the explosive growth of deep learning applications and increasing privacy concerns, the right to be forgotten has become a critical requirement in various AI industries. For example, given a facial recognition system, some individuals may wish to remove their personal data that might have been used in the training phase. Unfortunately, deep neural networks sometimes unexpectedly leak personal identities, making this removal challenging. While recent machine unlearning algorithms aim to enable models to forget specific data, we identify an unintended utility drop-correlation collapse-in which the essential correlations between image features and true labels weaken during the forgetting process. To address this challenge, we propose Distribution-Level Feature Distancing (DLFD), a novel method that efficiently forgets instances while preserving task-relevant feature correlations. Our method synthesizes data samples by optimizing the feature distribution to be distinctly different from that of forget samples, achieving effective results within a single training epoch. Through extensive experiments on facial recognition datasets, we demonstrate that our approach significantly outperforms state-of-the-art machine unlearning methods in both forgetting performance and model utility preservation.
Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols
Department of Artificial Intelligence, Korea University, South Korea（韩国大学人工智能系）；Computer Science Dept. at Courant Institute of Mathemtical Sciences, New York University, United States（纽约大学Courant数学科学研究所计算机科学系）
Bias Redistribution in Visual Machine Unlearning: Does Forgetting One Group Harm Another?
NewraLab；Beihang University（北京航空航天大学）；GoerTek Alpha Lab（歌尔Alpha实验室）；Skyline University Nigeria（尼日利亚天际线大学）；UESTC（电子科技大学）；Imperial College London（帝国理工学院）
SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Southeast University（东南大学）；Ant Group, Alibaba（蚂蚁集团）
ReLearn: Unlearning via Learning for Large Language Models
Zhejiang University（浙江大学）；Xiamen University（厦门大学）；Tsinghua University（清华大学）；Harbin Institute of Technology（哈尔滨工业大学）；National University of Singapore（新加坡国立大学）；NUS-NCS Joint Lab（新加坡国立大学-新科系统联合实验室）
针对现有大模型遗忘方法破坏文本连贯性与评估指标不足的问题，提出数据增强微调流水线 ReLearn 及包含 KFR、KRR 和 LS 的综合评估框架，实现了高效目标遗忘并保持高质量生成。
Forget-MI: Machine Unlearning for Forgetting Multimodal Information in Healthcare Settings
Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE Department of Computer Science, Michigan State University, United States 0.2cm
Train Once, Forget Precisely: Anchored Optimization for Efficient Post-Hoc Unlearning
University College London, London, United Kingdom（伦敦大学学院）；University of Nottingham, Nottingham, United Kingdom（诺丁汉大学）；Bharati Vidyapeeth College of Engineering, New Delhi, India（巴里蒂大学工程学院）
UnlearnShield: Shielding Forgotten Privacy against Unlearning Inversion
Huazhong University of Science and Technology（华中科技大学）；Institute of Guizhou Aerospace Measuring and Testing Technology（贵州航天测量测试技术研究院）；University of Technology Sydney（悉尼科技大学）；Griffith University（格里菲斯大学）
