Summary of An Architectural Approach to Enhance Deep Long-tailed Learning, by Yuhan Pan and Yanan Sun and Wei Gong
An Architectural Approach to Enhance Deep Long-Tailed Learning
by Yuhan Pan, Yanan Sun, Wei Gong
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 deep long-tailed recognition is presented, focusing on improving neural architectures rather than solely relying on data augmentation techniques. The authors utilize Differential Architecture Search (DARTS) to mitigate the effects of imbalanced data distributions and propose a new search space, Long-Tailed Differential Architecture Search (LTDAS), which incorporates superior architectural components. To address the limitations of existing DARTS methods in long-tailed scenarios, the proposed approach replaces the learnable linear classifier with an Equiangular Tight Frame (ETF) classifier. Experimental evaluations demonstrate that this approach achieves state-of-the-art results and consistently outperforms existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make computers better at recognizing things is being developed. This method focuses on changing how computer models are designed, rather than just trying to fix the data they’re trained on. The researchers use a technique called Differential Architecture Search (DARTS) to create better models and propose some changes to make it work even better with imbalanced data. They also suggest using a new type of classifier that helps prevent mistakes. This approach seems to be very good at recognizing things, even when the data is not balanced. |
Keywords
» Artificial intelligence » Data augmentation
