Summary of Stochastic Gradient Descent Jittering For Inverse Problems: Alleviating the Accuracy-robustness Tradeoff, by Peimeng Guan et al.
Stochastic Gradient Descent Jittering for Inverse Problems: Alleviating the Accuracy-Robustness Tradeoff
by Peimeng Guan, Mark A. Davenport
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 This paper proposes a new training scheme called SGD jittering for model-based architectures (MBAs) that aims to improve the accuracy-robustness tradeoff in inverse problems. Specifically, the method injects noise iteration-wise during reconstruction to enhance generalization and robustness against perturbations. The authors theoretically demonstrate that this approach outperforms standard mean squared error training and is more robust to average-case attacks. Experimental results show that SGD jittering achieves cleaner reconstructions for out-of-distribution data and demonstrates enhanced robustness to adversarial attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper wants to make sure that machines can accurately rebuild things we can’t see from messed up measurements. But it’s not just about making it work, it’s also important to make sure the results are correct even if the data is weird or fake. The researchers found a way to make model-based architectures (think of them like super smart robots) more reliable by adding some noise to how they learn. This new approach is better at guessing right and can handle tricky situations where the information is bad or trying to trick it. |
Keywords
» Artificial intelligence » Generalization
