Summary of Hierarchical Selective Classification, by Shani Goren et al.
Hierarchical Selective Classification
by Shani Goren, Ido Galil, Ran El-Yaniv
First submitted to arxiv on: 19 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 Hierarchical Selective Classification (HSC) to address uncertainty estimation in deep neural networks for risk-sensitive tasks. HSC leverages class relationships to reduce prediction specificity when faced with uncertainty. The approach formalizes hierarchical risk and coverage, develops inference rules, and guarantees target accuracy constraints. Empirical studies on ImageNet classifiers reveal that training regimes like CLIP, pretraining on ImageNet21k, and knowledge distillation boost HSC performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines make better decisions by predicting when they’re not sure. It introduces a new way to group related things together (like animals) and uses this grouping to make predictions less specific when it’s unsure. The method works with pictures and can be used in real-life applications like self-driving cars or medical diagnosis. |
Keywords
» Artificial intelligence » Classification » Inference » Knowledge distillation » Pretraining
