Summary of A Survey Of Neural Network Robustness Assessment in Image Recognition, by Jie Wang et al.
A Survey of Neural Network Robustness Assessment in Image Recognition
by Jie Wang, Jun Ai, Minyan Lu, Haoran Su, Dan Yu, Yutao Zhang, Junda Zhu, Jingyu Liu
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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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 abstract presents a survey on assessing the robustness of neural networks in image recognition tasks. It highlights the importance of robustness in artificial intelligence systems operating in complex environments. Researchers have developed techniques to evaluate robustness under deliberate adversarial attacks and random data corruptions. The survey provides an extensive overview of current research papers, analyzing concepts, metrics, and assessment methods. Key findings include a discussion on perturbation metrics and range representations used to measure image distortions, as well as robustness metrics for classification models. Strengths and limitations of existing methods are also presented, along with potential future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The survey explores the importance of neural network robustness in image recognition tasks. Researchers have developed techniques to test robustness against deliberate attacks and random data corruptions. The paper provides an overview of current research, looking at how researchers assess robustness. It talks about different ways to measure image distortions and how well models can classify images even when they’re a bit messed up. The paper also discusses what works well and what doesn’t in current methods. |
Keywords
» Artificial intelligence » Classification » Neural network
