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The Role of Meta-learning for Few-shot Learning
Eleni Triantafillou

Mon Dec 05 02:00 AM -- 04:30 AM (PST) @ Virtual

While deep learning has driven impressive progress, one of the toughest remaining challenges is generalization beyond the training distribution. Few-shot learning is an area of research that aims to address this, by striving to build models that can learn new concepts rapidly in a more "human-like" way. While many influential few-shot learning methods were based on meta-learning, recent progress has been made by simpler transfer learning algorithms, and it has been suggested in fact that few-shot learning might be an emergent property of large-scale models. In this talk, I will give an overview of the evolution of few-shot learning methods and benchmarks from my point of view, and discuss the evolving role of meta-learning for this problem. I will discuss lessons learned from using larger and more diverse benchmarks for evaluation and trade-offs between different approaches, closing with a discussion about open questions.

Link to slides: https://drive.google.com/file/d/1ZIULjhFjyNqjSS10p-5CDaqgzlrZcaGD/view?usp=sharing

Author Information

Eleni Triantafillou (Google Brain)

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