Summary of Learning From Reduced Labels For Long-tailed Data, by Meng Wei et al.
Learning from Reduced Labels for Long-Tailed Data
by Meng Wei, Zhongnian Li, Yong Zhou, Xinzheng Xu
First submitted to arxiv on: 25 Mar 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 A novel approach to weakly supervised learning for long-tailed data is introduced, tackling the issue of accuracy decline in tail classes. The Reduced Label setting preserves supervised information while reducing labeling costs. A straightforward and efficient framework with theoretical guarantees learns from these labels, outperforming state-of-the-art methods on ImageNet benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to learn from pictures without much human help is being explored. This helps reduce the effort needed for labeling data, especially when there are many classes with few examples each. The method, called Reduced Label, keeps track of important information while still reducing the cost of labeling. It’s been tested on well-known datasets and performs better than other methods that don’t require as much human help. |
Keywords
* Artificial intelligence * Supervised
