GO4Align: Group Optimization for Multi-Task Alignment
About
This paper proposes \textit{GO4Align}, a multi-task optimization approach that tackles task imbalance by explicitly aligning the optimization across tasks. To achieve this, we design an adaptive group risk minimization strategy, comprising two techniques in implementation: (i) dynamical group assignment, which clusters similar tasks based on task interactions; (ii) risk-guided group indicators, which exploit consistent task correlations with risk information from previous iterations. Comprehensive experimental results on diverse benchmarks demonstrate our method's performance superiority with even lower computational costs.
Jiayi Shen, Cheems Wang, Zehao Xiao, Nanne Van Noord, Marcel Worring• 2024
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Multi-task Learning | Cityscapes (test) | MR7 | 43 | |
| Multi-task Learning (Segmentation, Depth, Surface Normal) | NYU v2 (test) | mIoU40.42 | 14 | |
| Multi-task Learning (40 tasks) | CelebA (test) | Misclassification Rate3.1 | 12 | |
| Multi-task Learning (11 tasks) | QM9 (test) | MR (Mean Relative Error)4.55 | 12 |
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