Learning Condensed and Aligned Features for Unsupervised Domain Adaptation Using Label Propagation
This work addresses the labeling issue in supervised learning for domain adaptation, offering an incremental improvement over existing methods by enhancing feature alignment and cluster separability.
The paper tackles the problem of imperfect feature alignment and unclear clusters in unsupervised domain adaptation, proposing a method based on label propagation and cycle consistency that achieves improved accuracy by forming aligned and discriminative clusters.
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.