Summary of Robust Classification by Coupling Data Mollification with Label Smoothing, By Markus Heinonen et al.
Robust Classification by Coupling Data Mollification with Label Smoothing
by Markus Heinonen, Ba-Hien Tran, Michael Kampffmeyer, Maurizio Filippone
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 approach couples data mollification with label smoothing to enhance generalization in deep neural networks. By introducing noising and blurring to images, and aligning predicted label confidences with image degradation, the method improves robustness and uncertainty quantification on corrupted image benchmarks. The simplicity of implementation, negligible overheads, and combinability with existing augmentations make this technique a valuable addition to the toolkit for deep learning practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models are great at recognizing pictures, but they can get fooled by noisy or blurry images. To help them be more robust, researchers have come up with an idea called data mollification. This involves adding noise and blur to training images, just like how real-world images might look when they’re corrupted. The team also uses label smoothing to make the model’s predictions match the level of uncertainty in its guesses. This new approach is easy to use, doesn’t slow down the model too much, and can be combined with other techniques for even better results. |
Keywords
» Artificial intelligence » Deep learning » Generalization
