HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks

TL;DR

HydaLearn is a dynamic task weighting method for multi-task learning that adjusts loss weights at the mini-batch level based on task gradients, improving performance over static weighting schemes.

Contribution

The paper introduces HydaLearn, a novel algorithm that dynamically adjusts task weights during training based on gradient information, addressing relevance drift and mini-batch variability.

Findings

Performance improved on synthetic data

Enhanced results on two supervised learning domains

Effective handling of auxiliary task relevance drift

Abstract

Multi-task learning (MTL) can improve performance on a task by sharing representations with one or more related auxiliary-tasks. Usually, MTL-networks are trained on a composite loss function formed by a constant weighted combination of the separate task losses. In practice, constant loss weights lead to poor results for two reasons: (i) the relevance of the auxiliary tasks can gradually drift throughout the learning process; (ii) for mini-batch based optimisation, the optimal task weights vary significantly from one update to the next depending on mini-batch sample composition. We introduce HydaLearn, an intelligent weighting algorithm that connects main-task gain to the individual task gradients, in order to inform dynamic loss weighting at the mini-batch level, addressing i and ii. Using HydaLearn, we report performance increases on synthetic data, as well as on two supervised learning domains.