提出自辅助任务，实现任务间不对称知识迁移。
Enabling Asymmetric Knowledge Transfer in Multi-Task Learning with Self-Auxiliaries
- 引入自复制任务，灵活控制知识在任务间的单向传递。
- 在视觉基准任务上显著优于现有多任务优化方法。
- 适合存在强弱依赖关系的多任务场景，如少样本学习。
多任务学习中的知识迁移通常被视为二元对立：正向迁移提升所有任务性能，负向迁移则损害所有任务。本文研究了被忽视的不对称任务关系——某些任务受益于知识迁移，而另一些任务却因此受阻。为此，我们提出一种优化策略，在训练过程中加入名为自辅助（self-auxiliaries）的克隆任务，以灵活实现任务间的非对称知识转移。该方法可利用正向迁移优势，同时规避负向迁移影响。实验表明，在多个计算机视觉基准任务上，该方法相较现有策略显著提升性能。
原文摘要 · Abstract (English)
Knowledge transfer in multi-task learning is typically viewed as a dichotomy; positive transfer, which improves the performance of all tasks, or negative transfer, which hinders the performance of all tasks. In this paper, we investigate the understudied problem of asymmetric task relationships, where knowledge transfer aids the learning of certain tasks while hindering the learning of others. We propose an optimisation strategy that includes additional cloned tasks named self-auxiliaries into the learning process to flexibly transfer knowledge between tasks asymmetrically. Our method can exploit asymmetric task relationships, benefiting from the positive transfer component while avoiding the negative transfer component. We demonstrate that asymmetric knowledge transfer provides substantial improvements in performance compared to existing multi-task optimisation strategies on benchmark computer vision problems.
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