awesome-multi-task-learning and Awesome-Multi-Task-Learning
These are competing curated lists of the same subject matter, with A offering broader coverage (datasets, codebases, and papers) while B focuses primarily on published research works.
About awesome-multi-task-learning
thuml/awesome-multi-task-learning
A curated list of DATASETS, CODEBASES and PAPERS on Multi-Task Learning (MTL), from Machine Learning perspective.
Organizes MTL research across six dimensions—architectures (parameter sharing, modularity, task representation), optimization strategies (gradient balancing, task interference), datasets spanning vision/NLP/robotics/graphs, and theoretical foundations—with taxonomy covering hard/soft parameter sharing, mixture-of-experts routing, Pareto optimization, and task relationship learning. Aggregates benchmarks like Taskonomy (26 vision tasks), decaNLP, and MetaWorld alongside reference implementations in PyTorch (LibMTL, TorchJD) and domain-specific codebases for vision and recommendation systems.
About Awesome-Multi-Task-Learning
WeiHongLee/Awesome-Multi-Task-Learning
An up-to-date list of works on Multi-Task Learning
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