Injecting Imbalance Sensitivity for Multi-Task Learning

• URL: http://arxiv.org/abs/2503.08006v1
• Date: Tue, 11 Mar 2025 03:11:54 GMT
• Title: Injecting Imbalance Sensitivity for Multi-Task Learning
• Authors: Zhipeng Zhou, Liu Liu, Peilin Zhao, Wei Gong,
• Abstract summary: Multi-task learning (MTL) has emerged as a promising approach for deploying deep learning models in real-life applications.<n>Recent studies have proposed optimization-based learning paradigms to establish task-shared representations in MTL.<n>Our paper empirically argues that these studies primarily emphasize the conflict issue while neglecting the potentially more significant impact of imbalance/dominance in MTL.
• Score: 36.60453299563175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-task learning (MTL) has emerged as a promising approach for deploying deep learning models in real-life applications. Recent studies have proposed optimization-based learning paradigms to establish task-shared representations in MTL. However, our paper empirically argues that these studies, specifically gradient-based ones, primarily emphasize the conflict issue while neglecting the potentially more significant impact of imbalance/dominance in MTL. In line with this perspective, we enhance the existing baseline method by injecting imbalance-sensitivity through the imposition of constraints on the projected norms. To demonstrate the effectiveness of our proposed IMbalance-sensitive Gradient (IMGrad) descent method, we evaluate it on multiple mainstream MTL benchmarks, encompassing supervised learning tasks as well as reinforcement learning. The experimental results consistently demonstrate competitive performance.

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