Summary of Unified Gradient-based Machine Unlearning with Remain Geometry Enhancement, by Zhehao Huang et al.
Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement
by Zhehao Huang, Xinwen Cheng, JingHao Zheng, Haoran Wang, Zhengbao He, Tao Li, Xiaolin Huang
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine unlearning (MU) has emerged as a crucial technique for enhancing the privacy and trustworthiness of deep neural networks. Our investigation focuses on approximate MU, a practical approach for large-scale models. We identify the steepest descent direction by minimizing the output Kullback-Leibler divergence to exact MU within a parameter’s neighborhood. This decomposition into three components – weighted forgetting gradient ascent, fine-tuning retaining gradient descent, and a weight saliency matrix – encompasses most existing gradient-based MU methods. However, adhering to Euclidean space may lead to sub-optimal iterative trajectories due to the overlooked geometric structure of the output probability space. To address this issue, we propose embedding the unlearning update into a manifold rendered by the remaining geometry, incorporating second-order Hessian from the remaining data. This helps prevent effective unlearning from interfering with retained performance. A fast-slow parameter update strategy is proposed to efficiently leverage the benefits of Hessian modulation, making our approach adaptable across computer vision unlearning tasks, including classification and generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning researchers have created a new way to make neural networks more private and trustworthy. They did this by finding a path for the network’s parameters that minimizes changes to its output. This helps keep sensitive information hidden while still allowing the network to work well. The researchers found that most other methods for making neural networks forget are actually just different versions of their approach. |
Keywords
» Artificial intelligence » Classification » Embedding » Fine tuning » Gradient descent » Machine learning » Probability
