Summary of Go4align: Group Optimization For Multi-task Alignment, by Jiayi Shen et al.
GO4Align: Group Optimization for Multi-Task Alignment
by Jiayi Shen, Cheems Wang, Zehao Xiao, Nanne Van Noord, Marcel Worring
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper proposes GO4Align, a multi-task optimization approach that tackles task imbalance by explicitly aligning the optimization across tasks. The proposed method uses an adaptive group risk minimization strategy, comprising two techniques: dynamical group assignment and risk-guided group indicators. This approach clusters similar tasks based on task interactions and exploits consistent task correlations with risk information from previous iterations. The paper provides comprehensive experimental results on diverse benchmarks, demonstrating the method’s performance superiority with even lower computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GO4Align is a new way to solve multiple problems at once. The problem is that some of these problems are easier or harder than others, and this makes it hard for computers to do them all well. GO4Align fixes this by grouping similar problems together and using clues from previous attempts to help with the next ones. This makes it better at solving all the problems and uses less computer power. The results show that GO4Align does a great job on many different types of problems. |
Keywords
* Artificial intelligence * Multi task * Optimization
