Independent Component Alignment for Multi-task LearningDownload PDF

Published: 28 Jan 2022, Last Modified: 22 Oct 2023ICLR 2022 SubmittedReaders: Everyone
Keywords: multi-task learning, optimization
Abstract: We present a novel gradient-based multi-task learning (MTL) approach that balances training in multi-task systems by aligning the independent components of the training objective. In contrast to state-of-the-art MTL approaches, our method is stable and preserves the ratio of highly correlated tasks gradients. The method is scalable, reduces overfitting, and can seamlessly handle multi-task objectives with a large difference in gradient magnitudes. We demonstrate the effectiveness of the proposed approach on a variety of MTL problems including digit classification, multi-label image classification, camera relocalization, and scene understanding. Our approach performs favourably compared to other gradient-based adaptive balancing methods, and its performance is backed up by theoretical analysis.
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