learn2learn: A Library for Meta-Learning Research

Understand

Meta-learning researchers face two fundamental issues in their empirical work: prototyping and reproducibility.

• Researchers are prone to make mistakes when prototyping new algorithms and tasks because modern meta-learning methods rely on unconventional functionalities of machine learning frameworks.
• In turn, reproducing existing results becomes a tedious endeavour -- a situation exacerbated by the lack of standardized implementations and benchmarks.
• As a result, researchers spend inordinate amounts of time on implementing software rather than understanding and developing new ideas.