mlsanityData to 14 Oct 2026

LibMTL: A Python Library for Multi-Task Learning

arXiv 2203.14338 ↗First posted 27 Mar 2022cs.LGcited by 25

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Abstract (arXiv)

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.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 27 Mar 2022.

Builds on

13 of 21 references.

References · 13

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  3. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  4. 2017A Survey on Multi-Task Learningcited by 840
  5. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 834
  6. 2018Multi-Task Learning as Multi-Objective Optimizationcited by 821
  7. 2018End-to-End Multi-Task Learning with Attentioncited by 534
  8. 2016Cross-stitch Networks for Multi-task Learningcited by 467
  9. 2021Conflict-Averse Gradient Descent for Multi-task Learningcited by 459
  10. 2020Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropoutcited by 201
  11. 2020Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Modelscited by 169
  12. 2021DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learningcited by 115
1 more
  1. 2019MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learningcited by 45

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 9; 2024: 9; 2025: 3; 2026: 3.

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  1. 2023Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidancecited by 36
  2. 2023Addressing Negative Transfer in Diffusion Modelscited by 33
  3. 2023ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learningcited by 28
  4. 2023Dual-Balancing for Multi-Task Learningcited by 27
  5. 2024Robust Multi-Task Learning with Excess Riskscited by 24
  6. 2022Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approachcited by 14
  7. 2024A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimizationcited by 8
  8. 2023Multi-domain Recommendation with Embedding Disentangling and Domain Alignmentcited by 7
  9. 2023Sample-Level Weighting for Multi-Task Learning with Auxiliary Taskscited by 6
  10. 2023When Multi-Task Learning Meets Partial Supervision: A Computer Vision Reviewcited by 6
  11. 2024Analytical Uncertainty-Based Loss Weighting in Multi-Task Learningcited by 6
  12. 2024Multitask Learning in Minimally Invasive Surgical Vision: A Reviewcited by 5
13 more
  1. 2024radarODE-MTL: A Multi-Task Learning Framework with Eccentric Gradient Alignment for Robust Radar-Based ECG Reconstructioncited by 4
  2. 2024Label-free Neural Semantic Image Synthesiscited by 3
  3. 2024Federated Communication-Efficient Multi-Objective Optimizationcited by 3
  4. 2023Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removalcited by 2
  5. 2023Examining Common Paradigms in Multi-Task Learningcited by 2
  6. 2024Multi-Task Learning as enabler for General-Purpose AI-native RANcited by 2
  7. 2025EndoARSS: Adapting Spatially-Aware Foundation Model for Efficient Activity Recognition and Semantic Segmentation in Endoscopic Surgerycited by 1
  8. 2025SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbationcited by 1
  9. 2025Simplifying Multi-Task Architectures Through Task-Specific Normalizationcited by 1
  10. 2026Monroe: A Molecular Foundation Model for In-Context Probabilistic Inferencecited by 1
  11. 2024Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysiscited by 0
  12. 2026HyperCT: Low-Rank Hypernet for Unified Chest CT Analysiscited by 0
  13. 2026Towards Unified Multi-task EEG Analysis with Low-Rank Adaptationcited by 0