Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning

TL;DR

Rep-MTL introduces a novel approach that leverages representation-level task saliency to improve multi-task learning by promoting task complementarity and reducing negative transfer, leading to better performance.

Contribution

This paper proposes Rep-MTL, a new method that exploits representation-level task saliency to enhance multi-task learning beyond optimizer-centric techniques.

Findings

Achieves competitive performance on four MTL benchmarks.

Effectively balances task-specific learning and cross-task sharing.

Demonstrates improved task cooperation through entropy-based penalization.

Abstract

Despite the promise of Multi-Task Learning in leveraging complementary knowledge across tasks, existing multi-task optimization (MTO) techniques remain fixated on resolving conflicts via optimizer-centric loss scaling and gradient manipulation strategies, yet fail to deliver consistent gains. In this paper, we argue that the shared representation space, where task interactions naturally occur, offers rich information and potential for operations complementary to existing optimizers, especially for facilitating the inter-task complementarity, which is rarely explored in MTO. This intuition leads to Rep-MTL, which exploits the representation-level task saliency to quantify interactions between task-specific optimization and shared representation learning. By steering these saliencies through entropy-based penalization and sample-wise cross-task alignment, Rep-MTL aims to mitigate negative transfer by maintaining the effective training of individual tasks instead pure conflict-solving, while explicitly promoting complementary information sharing. Experiments are conducted on four challenging MTL benchmarks covering both task-shift and domain-shift scenarios. The results show that Rep-MTL, even paired with the basic equal weighting policy, achieves competitive performance gains with favorable efficiency. Beyond standard performance metrics, Power Law exponent analysis demonstrates Rep-MTL's efficacy in balancing task-specific learning and cross-task sharing. The project page is available at HERE.