Leveraging convergence behavior to balance conflicting tasks in multi-task learning

TL;DR

This paper introduces a dynamic multi-objective optimization method for multi-task learning that adjusts task importance based on gradient behavior, improving performance on conflicting tasks.

Contribution

It proposes a novel approach leveraging gradient temporal behavior to balance conflicting tasks in multi-task learning, outperforming existing methods.

Findings

Outperforms state-of-the-art methods on conflicting tasks

Ensures all tasks reach good generalization performance

Adapts task importance dynamically during training

Abstract

Multi-Task Learning is a learning paradigm that uses correlated tasks to improve performance generalization. A common way to learn multiple tasks is through the hard parameter sharing approach, in which a single architecture is used to share the same subset of parameters, creating an inductive bias between them during the training process. Due to its simplicity, potential to improve generalization, and reduce computational cost, it has gained the attention of the scientific and industrial communities. However, tasks often conflict with each other, which makes it challenging to define how the gradients of multiple tasks should be combined to allow simultaneous learning. To address this problem, we use the idea of multi-objective optimization to propose a method that takes into account temporal behaviour of the gradients to create a dynamic bias that adjust the importance of each task during the backpropagation. The result of this method is to give more attention to the tasks that are diverging or that are not being benefited during the last iterations, allowing to ensure that the simultaneous learning is heading to the performance maximization of all tasks. As a result, we empirically show that the proposed method outperforms the state-of-art approaches on learning conflicting tasks. Unlike the adopted baselines, our method ensures that all tasks reach good generalization performances.