Summary of Automatic Combination Of Sample Selection Strategies For Few-shot Learning, by Branislav Pecher et al.
Automatic Combination of Sample Selection Strategies for Few-Shot Learning
by Branislav Pecher, Ivan Srba, Maria Bielikova, Joaquin Vanschoren
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 investigates the impact of 20 sample selection strategies on the performance of five few-shot learning approaches across eight image and six text datasets. The authors thoroughly evaluate these strategies in various settings, including meta-learning, fine-tuning, and in-context learning. They also propose a novel method for combining sample selection strategies, which consistently outperforms individual methods. The results demonstrate significant dependence on modality, dataset, approach, and number of shots, highlighting the importance of sample selection strategies, especially for lower shot counts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to choose the right examples to train a model when you only have a few samples. They test 20 different ways to do this and see which ones work best in five different situations. The authors also come up with a new way to combine these strategies, which does even better than each one separately. This shows that choosing the right examples matters a lot, especially when you don’t have many. |
Keywords
* Artificial intelligence * Few shot * Fine tuning * Meta learning
