Summary of Episodic-free Task Selection For Few-shot Learning, by Tao Zhang
Episodic-free Task Selection for Few-shot Learning
by Tao Zhang
First submitted to arxiv on: 31 Jan 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 proposes a novel meta-training framework that goes beyond episodic training for few-shot learning. The traditional episodic training strategy is often outperformed by non-episodic approaches, such as Neighbourhood Component Analysis (NCA). To address this, the authors suggest selecting episodic-free tasks from a task set based on their affinity to the target tasks. This framework uses contrastive learning and classification as promising types of training tasks, and demonstrates its effectiveness on miniImageNet, tiered-ImageNet, and CIFAR-FS datasets using nearest centroid classifiers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to train machines to learn from very few examples. Usually, we train models by giving them many examples to practice with. But what if we only have a few examples? This can be a big problem for machines learning from images or other data. The authors of this paper found that using a different type of training, called “episodic-free tasks”, can actually help machines learn better from very few examples. They tested their approach on three different image datasets and it worked well. |
Keywords
* Artificial intelligence * Classification * Few shot
