Adversarial Mixup Unlearning

Authors: Zhuoyi PENG, Yixuan Tang, Yi Yang
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Machine Unlearning, Catastrophic Unlearning, Mixup Regularization, Adversarial Training

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 in an adversarial direction: generating examples that prompt the unlearner to reveal information that should be forgotten, while losing essential knowledge. Then the unlearner, guided by two other contrastive loss terms, processes the synthesized and real data jointly to ensure accurate unlearning without losing critical knowledge, overcoming catastrophic effects. Extensive evaluations across benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches, offering a robust solution to machine unlearning. This work not only deepens understanding of unlearning mechanisms but also lays the foundation for effective machine unlearning with mixup augmentation.

Decision: accept (poster)
Reviewer scores: 6, 3, 6

Reviews and discussion:
<thread>
creation date:
1730501705880

thread_type:
official_review

<thread_object>
creator: reviewer_kjxn

<content>
summary:
This paper addresses the challenge of machine unlearning focusing on minimizing the adverse effects of removing data points from forgotten labels. The forgetting process often disrupts the retention of other knowledge, reducing the model’s generalisation, particularly in the intermediate space between forgotten and retained samples.

To mitigate this issue, the authors use data mixup, generating challenging samples through training a generator. These mixed samples reveal forgotten information while disrupting the retention of remaining data, effectively reversing the unlearning process. The unlearner is then trained on these mixup and real samples using contrastive-learning-based losses to improve robustness and efficacy in unlearning.

strengths:
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.

weaknesses:
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 Transformer). I am curious on additional discussion on any computational challenges in scaling up to larger models/datasets and how the method might need to be adapted.

- For challenging mixup samples, the goal is to reveal forgetting samples while lose the remaining samples. In traditional mixup, using a λ close to 1 could align with this aim. I would be interested in understanding how such traditional mixup samples perform within the unlearning process. The authors could consider including an ablation experiment that replaces their adversarial mixup generator with traditional mixup using different fixed λ values (e.g. 0.9, 0.95, 0.99) and compared with their proposed method.

questions:
Please see the Weaknesses
</content>
</thread_object>

</thread>
<thread>
creation date:
1730630273345

thread_type:
official_review

<thread_object>
creator: reviewer_zx2z

<content>
summary:
The author proposes a generator unlarner framework, which uses mixup technology combined with a generator to generate hard samples to help the model retain knowledge about the remaining dataset in forgotten samples. This framework is trained using two contrastive learning loss terms, effectively addressing the issue of potentially losing critical information about retained data during the forgetting process. The author substantiates the effectiveness of the proposed scheme through experimental validation.

strengths:
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 experiment is commendable, providing richer evidence to support the effectiveness of the proposed scheme.

weaknesses:
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 experiment is not sufficient, and additional deletion results at varying ratios would enhance the credibility. Furthermore, the limited number of datasets and models raises concerns. I am particularly interested in whether the experimental outcomes with more complex models and datasets, such as ImageNet, would influence the results of the method.
3. There is a lack of epoch description of the experiment of the approach, and it remains unclear how many training rounds are necessary for convergence to achieve satisfactory results. The criteria for terminating training are crucial aspects that require discussion in the context of approximate forgetting.
4. The comparative experiments lack sufficient explanation of hyperparameters, and some state-of-the-art results appear somewhat low. I recommend that the author adhere to the hyperparameter guidelines provided in the original paper. While the author states, "if our reproduced results align with the previously reported statistics in Shen et al. (2024), we present their results; otherwise, we provide our results," I believe that directly copying most of the experimental data is unacceptable. The author should revise the baseline results to reflect actual experimental findings. By the way, TS does not require further training of the teacher model; instead, the original and initialized models should serve as the teacher models for the experiment. I suggest the author review the original control scheme rather than relying solely on the introduction in [1]. If I have misunderstood any aspects, I would appreciate clarification from the author. In summary, the author's experimental section appears overly influenced by [1], which raises doubts about the authenticity of the author's experimental workload.

[1] Shaofei Shen, Chenhao Zhang, Yawen Zhao, Alina Bialkowski, Weitong Chen, and Miao Xu. Label-agnostic forgetting: A supervision-free unlearning in deep models. arXiv preprint arXiv:2404.00506, 2024.

questions:
Please check the questions in the weaknesses above.
Additionally, why does the retrained model have a different number of epochs compared to the initial model? Why is the testing accuracy of the model higher than the training accuracy on the CIFAR-10 and SVHN datasets? Does this indicate that the model may not fully converge within the specified number of training rounds, and could certain fine-tuning experimental schemes be affecting the accuracy of the results? Although the author referenced the experimental setup in [1], I think it is essential to ensure that the original model achieves full convergence before drawing any conclusions in research related to unlearning.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730666215563

thread_type:
official_review

<thread_object>
creator: reviewer_mh1a

<content>
summary:
The paper introduces a novel and unique generator-unlearner method to yield mixed examples, which helps improve  machine unlearning effectiveness. Especially , this paper use a  novel contrastive objective not only to help refine generator, but also help unlearner  retain knowledge of remaining data and forget  forgetting data.  Hence, this operation can   prevent  catastrophic unlearning issue.  Furthermore, extensive experiments  demonstrate the efficiency and effectiveness of their proposed method.

strengths:
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.

weaknesses:
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.

questions:
Why MixUnlearn sometimes demonstrates better performance than retrain (e.g., on CIFAR-10)?
</content>
</thread_object>

</thread>
<thread>
creation date:
1732400268872

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Summary of Revision

comment:
**Summary of Revision**

We sincerely thank the reviewers for their detailed review, valuable feedback, and acknowledgment of the novelty of our work. To address the concerns raised and enhance the quality of our paper, we have made the following revisions:

1. **Expanded Experiments on Challenging Datasets**  
   - We conducted additional experiments using the ImageNet dataset and the ViT architecture, targeting more challenging scenarios. These include quantitative results, KDE plot visualizations, and comprehensive time-cost analyses.  
   - Details can be found in **Appendix A11**.

2. **Evaluation of Robustness Across Data Sizes**  
   - We benchmarked our method's performance across varying dataset sizes to demonstrate its robustness, regardless of the forgetting dataset size.  
   - The results are presented in **Appendix A10**.

3. **Reproduction of Benchmark Results (Clarifying No Differences in Findings)**  
   - Before first submission, we have reproduced all benchmark results rather than relying solely on cited experiment results from existing benchmarking in ICLR 24' paper [1]. Now, we use all our reproduced results in revised paper (only affecting some baselines but remaining similar findings). Our reproduced results closely match open benchmarks, except under noisy label conditions, where we provided our specific findings. Steps taken in **first submission** and **this revision** include:  
     - Reproducing open benchmark results with thorough parameter tuning before the initial submission.  
     - Reporting results from reproduced noisy label setups, noting differences.  
     - Introducing stronger baselines, such as **RandLabel** and **L-Mix**, for robust comparisons.  
     - Extending evaluations to include methods like **DSMixup** and **GLI**.  
     - Incorporating additional evaluation settings, such as semi-supervised scenarios.  
   - Full details and reproduced results are available in **Section 5.4**.

4. **Demonstrating Model Convergence**  
   - To provide evidence of model convergence, we added learning curves for our models.  
   - These are included in **Appendices A2 and A3**.

5. **Comprehensive Ablation Studies**  
   - We conducted ablation studies to analyze the effect of the mixup ratio, offering deeper insights into its influence on model performance.  

We remain open to providing further results or clarifications to address any additional concerns. Thank you once again for your thoughtful review and constructive feedback.

[1] Shen, Shaofei, et al. "Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models." The Twelfth International Conference on Learning Representations.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732504653589

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Kind Reminder: Seeking Reviewer Feedback for Author/Reviewer Discussion Phase

comment:
Dear Reviewers,

We sincerely appreciate your invaluable feedback, which has been instrumental in improving our submission. In response to your reviews, we have thoughtfully addressed each of your comments and hope our revisions meet your expectations.

As the author/reviewer discussion phase (Nov 12–26) deadline is approaching, we kindly seek your input on our responses. We understand your time is limited and greatly value your insights. Engaging in further discussion would be immensely helpful in ensuring our submission aligns with the high standards of ICLR 2025.

Thank you again for your time and consideration. We are eager to collaborate further and refine our work.

Best regards,

The Authors
</content>
</thread_object>

</thread>


Metareview:
metareview:
The paper presents an algorithm for machine unlearning, focusing on the interface between the forget set and remain set. A mixup-based generator in combination with contrastive losses are used to sharpen the model's decision boundary between the forget and remain set by incorporating hard synthetic samples into the unlearning process. Experiments on a range of standard unlearning datasets with the addition of ImageNet show the effectiveness of the method.

The paper introduces a novel adversarial process and contrastive losses and extensive evaluation on multiple datasets including ImageNet that validate the effectiveness of the approach. The method also applies to label-agnostic unlearning in addition to label-aware unlearning. Overall it presents a good empirical study.