Summary of Robustness Reprogramming For Representation Learning, by Zhichao Hou et al.
Robustness Reprogramming for Representation Learning
by Zhichao Hou, MohamadAli Torkamani, Hamid Krim, Xiaorui Liu
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 The proposed novel non-linear robust pattern matching technique offers a reprogrammable approach to enhance the robustness of well-trained deep learning models against adversarial or noisy input perturbations without altering their parameters. The method is demonstrated to be effective across various learning models, including linear and convolutional neural networks. This work presents three model reprogramming paradigms for flexible control of robustness under different efficiency requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers explored a way to make well-trained deep learning models more robust against noisy or malicious input without changing the original model’s parameters. They developed a new method that can be applied to various types of neural networks, including simple and complex ones. The goal is to create AI systems that can withstand unexpected or unwanted inputs. |
Keywords
» Artificial intelligence » Deep learning » Pattern matching
