Summary of A Complete Survey on Contemporary Methods, Emerging Paradigms and Hybrid Approaches For Few-shot Learning, by Georgios Tsoumplekas et al.
A Complete Survey on Contemporary Methods, Emerging Paradigms and Hybrid Approaches for Few-Shot Learning
by Georgios Tsoumplekas, Vladislav Li, Panagiotis Sarigiannidis, Vasileios Argyriou
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper presents a comprehensive survey of Few-Shot Learning (FSL), a paradigm that enables rapid adaptation to novel learning tasks by leveraging prior knowledge. FSL is particularly well-suited for real-world applications where data is scarce, as it reduces the intense requirements for vast amounts of data and extensive training typically needed for deep learning. The survey covers both established methods and recent advancements in the field, including emerging paradigms such as in-context learning and novel categories within the meta-learning framework. It also explores FSL’s diverse applications across various domains and discusses recent trends shaping the field, outstanding challenges, and promising future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FSL helps machines learn new things with just a little practice. Right now, deep learning is super powerful, but it needs lots of data to work well. FSL fixes this by using what we already know to quickly learn new tasks. This paper looks at all the different ways scientists are trying to do this, and how they’re doing it. It also talks about where this technology might be useful, like in real-world situations where there isn’t much data. |
Keywords
* Artificial intelligence * Deep learning * Few shot * Meta learning
