Improve Unsupervised Domain Adaptation with Mixup Training
This work addresses the challenge of domain adaptation for machine learning models when labeled data is scarce in target domains, representing an incremental advancement over existing methods.
The paper tackles the problem of insufficient target domain performance in unsupervised domain adaptation by proposing a framework that enforces training constraints across domains using mixup formulation and a feature-level consistency regularizer, achieving significant improvements in state-of-the-art performance on image classification and human activity recognition tasks.
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.