Title: UES: An Ultra-expanded Semantic Space for Unsupervised Domain Adaptation
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Unsupervised Domain Adaptation, Transfer Learning, Margin loss

Abstract: Unsupervised domain adaptation (UDA) provides a promising solution for label 
annotation and dataset bias issues by facilitating knowledge transfer 
from a label-rich source domain to a related but unlabeled target 
domain. FC+Softmax+CE loss is the most commonly used supervised 
component in discriminative models. While it demonstrates unparalleled 
advantages under IID assumption, it fails to construct a metric 
space with better generalization performance. Therefore, under 
the non-IID assumption in UDA, the target domain features often 
exhibit large intra-class variations, which can easily cross decision 
boundaries and lead to inter-class confusion. To tackle this issue, 
we propose an innovative margin loss, Ultra-expanded (UE) loss, to 
encourage feature to occupy a more expansive space, thereby keeping 
them farther from decision boundaries. Furthermore, we propose a 
semantic zoom mechanism that enables the network to preserve key semantics. The combined Ultra-expanded Semantic (UES) loss constructs 
an Ultra-expanded semantic space and can be readily integrated into 
various UDA methods as a regularization term. Extensive experiments 
demonstrate its absolute performance improvements.

TL;DR: In UDA, the farther a feature is from the decision boundary, the better.