Improve Unsupervised Domain Adaptation with Mixup Training

TL;DR

This paper introduces a novel mixup-based training framework for unsupervised domain adaptation that enforces constraints across source and target domains, leading to improved performance in image classification and activity recognition.

Contribution

The work proposes a mixup formulation with feature-level regularization to enhance domain adaptation by explicitly modeling inter-domain relationships.

Findings

Significant performance improvements on image classification tasks.

Enhanced results in human activity recognition.

Effective handling of large domain discrepancies.

Abstract

Unsupervised domain adaptation studies the problem of utilizing a relevant source domain with abundant labels to build predictive modeling for an unannotated target domain. Recent work observe that the popular adversarial approach of learning domain-invariant features is insufficient to achieve desirable target domain performance and thus introduce additional training constraints, e.g. cluster assumption. However, these approaches impose the constraints on source and target domains individually, ignoring the important interplay between them. In this work, we propose to enforce training constraints across domains using mixup formulation to directly address the generalization performance for target data. In order to tackle potentially huge domain discrepancy, we further propose a feature-level consistency regularizer to facilitate the inter-domain constraint. When adding intra-domain mixup and domain adversarial learning, our general framework significantly improves state-of-the-art performance on several important tasks from both image classification and human activity recognition.