Learning Condensed and Aligned Features for Unsupervised Domain Adaptation Using Label Propagation

TL;DR

This paper introduces a novel unsupervised domain adaptation method that uses label propagation and cycle consistency to create aligned, discriminative feature clusters, improving adaptation performance.

Contribution

The method uniquely combines label propagation with cycle consistency to enhance feature alignment and cluster clarity in unsupervised domain adaptation.

Findings

Improved domain adaptation accuracy across various scenarios.

Features form well-aligned, separable clusters.

Visualization confirms better feature alignment.

Abstract

Unsupervised domain adaptation aiming to learn a specific task for one domain using another domain data has emerged to address the labeling issue in supervised learning, especially because it is difficult to obtain massive amounts of labeled data in practice. The existing methods have succeeded by reducing the difference between the embedded features of both domains, but the performance is still unsatisfactory compared to the supervised learning scheme. This is attributable to the embedded features that lay around each other but do not align perfectly and establish clearly separable clusters. We propose a novel domain adaptation method based on label propagation and cycle consistency to let the clusters of the features from the two domains overlap exactly and become clear for high accuracy. Specifically, we introduce cycle consistency to enforce the relationship between each cluster and exploit label propagation to achieve the association between the data from the perspective of the manifold structure instead of a one-to-one relation. Hence, we successfully formed aligned and discriminative clusters. We present the empirical results of our method for various domain adaptation scenarios and visualize the embedded features to prove that our method is critical for better domain adaptation.