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2023 JMLR JMLR 2023

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. [abs] [ pdf ][ bib ] [ code ] © JMLR 2023. (edit, beta)

📈 Trend Setter — Model Merging
🧭 Keyword Pioneer — optimization strategy
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning

Authors

Baijiong Lin , Yu Zhang

Topics

Machine Learning > Optimization & Theory > Optimization Machine Learning > Application Areas > Model Merging Machine Learning > Learning Types > Deep Learning Machine Learning > Learning Paradigms > Multi-Task Learning Deep Learning > Learning Types > Multi-Task Learning

Keywords

neural network architecture multi-task learning model architecture deep learning deep neural network optimization strategy python library
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