论文标题
Learn2Learn：元学习研究库
learn2learn: A Library for Meta-Learning Research
论文作者
论文摘要
元学习研究人员在经验工作中面临两个基本问题：原型和可重复性。在制作新算法和任务时，研究人员很容易犯错误，因为现代的元学习方法依赖于机器学习框架的非常规的功能。反过来，再现现有结果成为一项乏味的努力 - 由于缺乏标准化的实施和基准，这种情况加剧了这种情况。结果，研究人员花费了大量时间来实施软件，而不是理解和发展新想法。 该手稿介绍了Learn2Learn，这是一个用于元学习研究的库，重点是解决这些原型和可重复性问题。 Learn2Learn提供了在广泛的元学习技术（例如元淡入，元提升学习，少量学习）中提供常见的低级例程，并在其顶部构建了标准化的接口，以构建算法和基准的标准化接口。在免费和开源许可下释放Learn2Learn时，我们希望围绕标准化软件培养一个社区，以进行元学习研究。
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. This manuscript introduces learn2learn, a library for meta-learning research focused on solving those prototyping and reproducibility issues. learn2learn provides low-level routines common across a wide-range of meta-learning techniques (e.g. meta-descent, meta-reinforcement learning, few-shot learning), and builds standardized interfaces to algorithms and benchmarks on top of them. In releasing learn2learn under a free and open source license, we hope to foster a community around standardized software for meta-learning research.
