Summary of Towards Few-shot Learning in the Open World: a Review and Beyond, by Hui Xue et al.
Towards Few-Shot Learning in the Open World: A Review and Beyond
by Hui Xue, Yuexuan An, Yongchun Qin, Wenqian Li, Yixin Wu, Yongjuan Che, Pengfei Fang, Minling Zhang
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper presents a comprehensive review of recent advancements in few-shot learning (FSL) designed to adapt FSL for use in open-world settings. It categorizes existing methods into three distinct types: varying instances, varying classes, and varying distributions. Each category is discussed in terms of its specific challenges and methods, as well as its strengths and weaknesses. The paper also standardizes experimental settings and metric benchmarks across scenarios, providing a comparative analysis of the performance of various methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to help machines learn like humans do by quickly picking up new concepts from just a few examples. But most current approaches rely on unrealistic assumptions about data being clean, complete, and unchanged. The paper shows how recent advances can be used in real-world situations where data is often uncertain, incomplete, or changing. |
Keywords
* Artificial intelligence * Few shot
