Summary of Unleashing the Power Of Multi-task Learning: a Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras, by Jun Yu et al.
Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras
by Jun Yu, Yutong Dai, Xiaokang Liu, Jin Huang, Yishan Shen, Ke Zhang, Rong Zhou, Eashan Adhikarla, Wenxuan Ye, Yixin Liu, Zhaoming Kong, Kai Zhang, Yilong Yin, Vinod Namboodiri, Brian D. Davison, Jason H. Moore, Yong Chen
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Multitask Learning (MTL) is a paradigm that leverages both task-specific and shared information to address multiple related tasks simultaneously. Unlike Single-Task Learning (STL), MTL offers benefits such as streamlined model architecture, performance enhancement, and cross-domain generalizability. Over the past twenty years, MTL has been widely recognized in various fields like Computer Vision (CV), Natural Language Processing (NLP), recommendation systems, disease prognosis and diagnosis, and robotics. This survey provides a comprehensive overview of MTL’s evolution, covering cutting-edge methods from traditional approaches to deep learning and pretrained foundation models. The survey categorizes MTL techniques into five key areas: regularization, relationship learning, feature propagation, optimization, and pre-training. It also explores the concepts of task-promptable and -agnostic training, as well as Zero-Shot Learning (ZSL), which unlocks the potential of this historically coveted learning paradigm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a special way to learn many things at once called Multitask Learning. It’s different from usual learning because it uses information from all tasks together, not just one task. This makes it more efficient and helps with learning new tasks. MTL has been used in many areas like computer vision, language processing, and health diagnosis. The paper shows how MTL has developed over time and what the latest techniques are. It also talks about new ways to use MTL that could lead to big breakthroughs. |
Keywords
» Artificial intelligence » Deep learning » Natural language processing » Nlp » Optimization » Regularization » Zero shot
