论文标题
libmtl：用于多任务学习的Python库
LibMTL: A Python Library for Multi-Task Learning
论文作者
论文摘要
本文介绍了LIBMTL，这是一个建立在Pytorch上的开源Python库，该图书馆为多任务学习（MTL）提供了统一，全面，可重现和可扩展的实现框架。 LIBMTL考虑了MTL中的不同设置和方法，它支持大量最先进的MTL方法，包括12种减肥策略，7种体系结构和84种不同体系结构的组合和减少权重方法。此外，LIBMTL中的模块化设计使其易于使用且可扩展，因此用户可以轻松，快速开发新的MTL方法，公平地与现有的MTL方法进行比较，或在LIBMTL的支持下将MTL算法应用于现实世界应用程序。 LIBMTL的源代码和详细文档可在https://github.com/median-research-group/libmtl和https://libmtl.readthedocs.io中获得。
This paper presents LibMTL, an open-source Python library built on PyTorch, which provides a unified, comprehensive, reproducible, and extensible implementation framework for Multi-Task Learning (MTL). LibMTL considers different settings and approaches in MTL, and it supports a large number of state-of-the-art MTL methods, including 12 loss weighting strategies, 7 architectures, and 84 combinations of different architectures and loss weighting methods. Moreover, the modular design in LibMTL makes it easy-to-use and well extensible, thus users can easily and fast develop new MTL methods, compare with existing MTL methods fairly, or apply MTL algorithms to real-world applications with the support of LibMTL. The source code and detailed documentations of LibMTL are available at https://github.com/median-research-group/LibMTL and https://libmtl.readthedocs.io, respectively.
