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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated list of DATASETS, CODEBASES and PAPERS on Multi-Task Learning (MTL), from Machine Learning perspective.
| Date | Stars |
|---|---|
| 2026-07-24 | 842 |
| 2026-07-25 | 842 |
| 2026-07-28 | 841 |
| 2026-07-30 | 841 |
| 2026-07-31 | 841 |
| 2026-08-08 | 840 |
| 2026-08-10 | 840 |
| 2026-08-11 | 840 |
| 2026-08-14 | 841 |
| 2026-08-18 | 841 |
| 2026-09-05 | 840 |
| 2026-09-12 | 841 |
| 2026-09-14 | 842 |
| 2026-09-20 | 842 |
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# Awesome Multi-Task Learning A curated list of datasets, codebases, and papers on Multi-Task Learning (MTL), from a Machine Learning perspective. This project greatly appreciates the surveys below, which have been incredibly helpful. We welcome your contributions! If you find any mistakes or omissions, please let us know. **Contact**: [Jialong Wu](https://manchery.github.io/) ## Table of Contents <details> <summary>Awesome Multi-Task Learning</summary> - [Survey](#survey) - [Benchmark & Dataset](#benchmark--dataset) - [Computer Vision](#computer-vision) - [NLP](#nlp) - [RL & Robotics](#rl--robotics) - [Graph](#graph) - [Recommendation](#recommendation) - [Codebase](#codebase) - [Architecture](#architecture) - [Hard Parameter Sharing](#hard-parameter-sharing) - [Soft Parameter Sharing](#soft-parameter-sharing) - [Decoder-focused Model](#decoder-focused-model) - [Modulation & Adapters](#modulation--adapters) - [Modularity, MoE, Routing & NAS](#modularity-moe-routing--nas) - [Task Representation](#task-representation) - [Others](#others) - [Optimization](#optimization) - [Loss & Gradient Strategy](#loss--gradient-strategy) - [Task Interference](#task-interference) - [Task Sampling](#task-sampling) - [Adversarial Training](#adversarial-training) - [Pareto](#pareto) - [Distillation](#distillation) - [Consistency](#consistency) - [Task Relationship Learning: Grouping, Tree (Hierarchy) & Cascading](#task-relationship-learning-grouping-tree-hierarchy--cascading) - [Theory](#theory) - [Misc](#misc) </details> ## Survey - ✨ Yu, J., Dai, Y., Liu, X., Huang, J., Shen, Y., Zhang, K., ... & Chen, Y. [Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras](https://arxiv.org/abs/2404.18961). ArXiv, 2024. - ✨ Vandenhende, S., Georgoulis, S., Proesmans, M., Dai, D., & Van Gool, L. [Multi-Task Learning for Dense Prediction Tasks: A Survey](https://arxiv.org/abs/2004.13379). TPAMI, 2021. - Crawshaw, M. [Multi-Task Learning with Deep Neural Networks: A Survey](http://arxiv.org/abs/2009.09796). ArXiv, 2020. - Worsham, J., & Kalita, J. [Multi-task learning for natural language processing in the 2020s: Where are we going?](https://doi.org/10.1016/j.patrec.2020.05.031) *Pattern Recognition Letters*, 2020. - Gong, T., Lee, T., Stephenson, C., Renduchintala, V., Padhy, S., Ndirango, A., Keskin, G., & Elibol, O. H. [A Comparison of Loss Weighting Strategies for Multi task Learning in Deep Neural Networks](https://ieeexplore.ieee.org/document/8848395). IEEE Access, 2019. - Li, J., Liu, X., Yin, W., Yang, M., Ma, L., & Jin, Y. [Empirical Evaluation of Multi-task Learning in Deep Neural Networks for Natural Language Processing](https://link.springer.com/article/10.1007/s00521-020-05268-w). Neural Computing and Applications, 2021. - ✨ Ruder, S. [An Overview of Multi-Task Learning in Deep Neural Networks](http://arxiv.org/abs/1706.05098). ArXiv, 2017. - ✨ Zhang, Y., & Yang, Q. [A Survey on Multi-Task Learning](https://ieeexplore.ieee.org/abstract/document/9392366). IEEE TKDE, 2021. ## Benchmark & Dataset ### Computer Vision - MultiMNIST / MultiFashionMNIST - a multitask variant of the MNIST / FashionMNIST dataset - ⚠️ *Toy datasets* - See: [MGDA](http://arxiv.org/abs/1810.04650), [Pareto MTL](http://papers.nips.cc/paper/9374-pareto-multi-task-learning.pdf), [IT-MTL](https://arxiv.org/abs/2010.15413), *etc*. - ✨ NYUv2 [[URL](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)] - 3 Tasks: Semantic Segmentation, Depth Estimation, Surface Normal Estimation - Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012). [Indoor Segmentation and Support Inference from RGBD Images](https://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf). ECCV, 2012. - ✨ CityScapes [[URL](https://www.cityscapes-dataset.com/)] - 3 Tasks: Semantic Segmentation, Instance Segmentation, Depth Estimation - ✨ PASCAL Context [[URL]
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7d474be8776509d7, topic:computer-vision, readme:computer vision, readme:semantic segmentation
matched fp:7d474be8776509d7, topic:deep-learning
matched fp:7d474be8776509d7, topic:awesome-list, desc:curated list, readme:curated list