Gradient Harmonization in Unsupervised Domain Adaptation

TL;DR

This paper introduces Gradient Harmonization techniques GH and GH++ to resolve gradient conflicts in unsupervised domain adaptation, improving model performance by better balancing domain alignment and classification tasks.

Contribution

The paper proposes novel Gradient Harmonization methods GH and GH++, which effectively mitigate gradient conflicts in UDA, enhancing existing models without requiring major changes.

Findings

GH and GH++ improve baseline UDA models.

GH++ minimizes deviation from original gradients.

Methods are compatible with various UDA models.

Abstract

Unsupervised domain adaptation (UDA) intends to transfer knowledge from a labeled source domain to an unlabeled target domain. Many current methods focus on learning feature representations that are both discriminative for classification and invariant across domains by simultaneously optimizing domain alignment and classification tasks. However, these methods often overlook a crucial challenge: the inherent conflict between these two tasks during gradient-based optimization. In this paper, we delve into this issue and introduce two effective solutions known as Gradient Harmonization, including GH and GH++, to mitigate the conflict between domain alignment and classification tasks. GH operates by altering the gradient angle between different tasks from an obtuse angle to an acute angle, thus resolving the conflict and trade-offing the two tasks in a coordinated manner. Yet, this would cause both tasks to deviate from their original optimization directions. We thus further propose an improved version, GH++, which adjusts the gradient angle between tasks from an obtuse angle to a vertical angle. This not only eliminates the conflict but also minimizes deviation from the original gradient directions. Finally, for optimization convenience and efficiency, we evolve the gradient harmonization strategies into a dynamically weighted loss function using an integral operator on the harmonized gradient. Notably, GH/GH++ are orthogonal to UDA and can be seamlessly integrated into most existing UDA models. Theoretical insights and experimental analyses demonstrate that the proposed approaches not only enhance popular UDA baselines but also improve recent state-of-the-art models.