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

Understand

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.