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GO4Align: Group Optimization for Multi-Task Alignment
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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.
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Rep-MTL regularizes the shared representation space of multi-task networks by adding an entropy penalty on task saliency and a contrastive alignment between tasks, reporting competitive gains on NYUv2, Cityscapes, Off...
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