Awesome Multi-Task Learning
The "Awesome Multi-Task Learning" project is a comprehensive compilation of resources focusing on Multi-Task Learning (MTL) from a machine learning standpoint. It offers an extensive collection of datasets, codebases, research papers, and more, all curated to aid anyone interested in exploring or working within the multi-task learning domain. The project also extends an open invitation for contributions from the wider community, aiming for continuous improvement and incorporation of new ideas.
Overview
The project is structured around several key areas of multi-task learning, with a detailed breakdown of the contents and themes covered. These areas include surveys, benchmarks and datasets, codebases, architectures, optimization strategies, task relationship learning, theories, and miscellaneous topics.
Survey
The surveys listed in this project summarize the landscape of multi-task learning. They range from general overviews to specialized studies in different applications, such as natural language processing and dense prediction tasks. Notable examples include works that explore the evolution of multi-task learning through various eras, including traditional, deep, and pretrained foundation model approaches.
Benchmark & Dataset
The project catalogues a wide array of benchmarks and datasets categorized by application areas like computer vision, natural language processing (NLP), reinforcement learning (RL) and robotics, graph learning, and recommendation systems. Each dataset comes with a brief description and, where applicable, references to significant publications that have utilized these datasets.
-
Computer Vision: Some examples include MultiMNIST and CityScapes, focusing on tasks like semantic and instance segmentation, depth estimation, among others.
-
NLP: Datasets such as GLUE and decaNLP are highlighted, designed for evaluating general language understanding and multi-task challenges.
-
RL & Robotics: Resources like the MetaWorld are included, which are instrumental in RL research.
-
Graph and Recommendation: QM9 for molecular properties and AliExpress for commercial recommendation tasks illustrate the diversity of datasets enlisted here.
Codebase
The codebases section is a treasure trove of implementations for various multi-task learning architectures and applications. It covers general libraries like LibMTL, specific implementations for computer vision, NLP systems like mt-dnn, and even specialized libraries for recommendations and reinforcement learning.
Architecture
The architecture section dives into different models and strategies employed in multi-task learning, elucidating concepts like:
-
Hard and Soft Parameter Sharing: Exploring techniques where parameters are either strictly shared or softly shared between tasks.
-
Decoder-focused Models: A focus on models that enhance performance by optimizing decoders to cater to specific tasks.
-
Modulation & Adapters: Techniques for adapting pre-trained models to new tasks without extensive retraining.
Optimization
In the optimization section, strategies for improving task efficiencies and performances are detailed, comprising approaches such as loss and gradient strategies, task interference mitigation, task sampling, and advanced methods like adversarial training and distillation.
Task Relationship Learning
This area indicates approaches and frameworks designed for understanding and exploiting relationships between tasks, beneficial in enhancing model learning.
Conclusion
The Awesome Multi-Task Learning initiative is a pivotal resource, compiled to bolster research and development in MTL. Researchers, students, and practitioners can find the latest methodologies, datasets, and supportive libraries to aid them in exploring this dynamically evolving domain. The project underscores the importance of collaboration, urging the community to contribute by pointing out omissions or errors and by introducing novel resources.