Summary of Robust Image Classification in the Presence Of Out-of-distribution and Adversarial Samples Using Attractors in Neural Networks, by Nasrin Alipour et al.
Robust Image Classification in the Presence of Out-of-Distribution and Adversarial Samples Using Attractors in Neural Networks
by Nasrin Alipour, Seyyed Ali SeyyedSalehi
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 approach uses a fully connected neural network trained to use training samples as attractors, enhancing its robustness in classifying inputs and identifying out-of-distribution (OOD) samples. The method achieves high accuracy in detecting OOD samples from MNIST, fashion-MNIST, and CIFAR-10-bw datasets, even when faced with severe adversarial attacks. Robust classification is critical for ensuring the suitability of deep neural networks in safety-critical systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a way to help computers classify things correctly even when they’re shown pictures or data that are very different from what they’ve seen before. The approach uses a special type of neural network that’s really good at telling the difference between normal and unusual data. It works well on lots of different types of data, including some that are intentionally trying to trick it. This is important because computers need to be able to make accurate decisions even when they’re shown unexpected or unusual information. |
Keywords
» Artificial intelligence » Classification » Neural network
