LibMTL: A Python Library for Deep Multi-Task Learning

Abstract

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 13 optimization strategies and 8 architectures. 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.

Cite

Text

Lin and Zhang. "LibMTL: A Python Library for Deep Multi-Task Learning." Machine Learning Open Source Software, 2023.

Markdown

[Lin and Zhang. "LibMTL: A Python Library for Deep Multi-Task Learning." Machine Learning Open Source Software, 2023.](https://mlanthology.org/mloss/2023/lin2023jmlr-libmtl/)

BibTeX

@article{lin2023jmlr-libmtl,
  title     = {{LibMTL: A Python Library for Deep Multi-Task Learning}},
  author    = {Lin, Baijiong and Zhang, Yu},
  journal   = {Machine Learning Open Source Software},
  year      = {2023},
  pages     = {1-7},
  volume    = {24},
  url       = {https://mlanthology.org/mloss/2023/lin2023jmlr-libmtl/}
}