Adversarial Mixup Unlearning
Zhuoyi Peng, Yixuan Tang, Yi Yang
TL;DR
This paper introduces MixUnlearn, a novel generator-unlearner framework using adversarial mixup samples to improve machine unlearning, effectively removing sensitive data while preserving essential model knowledge.
Contribution
The paper proposes a new adversarial mixup-based approach with a generator-unlearner framework and contrastive objectives to enhance machine unlearning and prevent catastrophic forgetting.
Findings
Outperforms state-of-the-art unlearning methods on benchmark datasets.
Effectively removes targeted data without losing critical model knowledge.
Demonstrates robustness against catastrophic unlearning effects.
Abstract
Machine unlearning is a critical area of research aimed at safeguarding data privacy by enabling the removal of sensitive information from machine learning models. One unique challenge in this field is catastrophic unlearning, where erasing specific data from a well-trained model unintentionally removes essential knowledge, causing the model to deviate significantly from a retrained one. To address this, we introduce a novel approach that regularizes the unlearning process by utilizing synthesized mixup samples, which simulate the data susceptible to catastrophic effects. At the core of our approach is a generator-unlearner framework, MixUnlearn, where a generator adversarially produces challenging mixup examples, and the unlearner effectively forgets target information based on these synthesized data. Specifically, we first introduce a novel contrastive objective to train the generator…
Peer Reviews
Decision·ICLR 2025 Poster
1. This paper uses a intriguing adversarial process to solve the maching unlearning issue. They use a unique generator-unlearner adversarial process to force unlearner to forget the forgetting and retain the remaining. This proposed method are novel and effective. 2. The proposed method outperforms previous work in a variety of settings, including both label-agnostic and label-aware settings, and has a relatively small time cost.
1. Though their experiments are relatively comprehensive, the underlying dataset are somehow small. More experimental results on large dataset are expected to be demonstrated (e.g. imagenet). 2. The presentation of the paper needs to be improved, especially the description of the methods section.
1. The paper is well-organized and easy to follow, figures and tables are helpful and easy-to-understand. This scheme is relatively simple yet highly effective, as demonstrated by the experimental results, and the designed loss function is particularly noteworthy. 2. The solution proposed by the author applies not only to traditional unlearning scenarios involving labels but also to novel label-agnostic scenarios, demonstrating a broad range of applicability. 3. The author's visualization of the
1. The design of the author's approach does not convincingly demonstrate an actual improvement in unlearning, despite the experimental results resembling retraining. Since retraining does not engage with forgotten data, any generalization enhancements derived from this data will inevitably be lost; otherwise, complete unlearning cannot be assured. The solution proposed by the author incorporates information from forgotten data, which intuitively undermines the concept of complete unlearning. 2.
The paper is well-organised, with clear motivations, methodology, and results. The research addresses a relevant problem and is sound, with a well-motivated toy example that illustrates the proposed approach effectively. The experimental results provide an adequate comparison to existing methods, that support the method's effectiveness.
I have no major concerns regarding this work. However, I do have two extended questions: - I understand that the authors follow experimental setups from previous works on machine unlearning, focusing on low-resolution images and older network architectures. While this is consistent with prior works, these setups may not fully reflect recent advancements in deep learning. I suggest authors to conduct experiments on larger datasets (e.g. ImageNet) and newer model architectures (e.g. ViT, Swin Tra
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications · Fault Detection and Control Systems