313 citations · 474 across the 22 of their papers we have counts for
4 papers · 1 filter
Known-class Aware Self-ensemble for Open Set Domain Adaptation
Qing Lian, Wen Li, Lin Chen +1
Existing domain adaptation methods generally assume different domains have the identical label space, which is quite restrict for real-world applications. In this paper, we focus o…
Semi-Supervised Learning by Augmented Distribution Alignment
Qin Wang, Wen Li, Luc Van Gool
In this work, we propose a simple yet effective semi-supervised learning approach called Augmented Distribution Alignment. We reveal that an essential sampling bias exists in semi-…
Sliced Wasserstein Generative Models
Jiqing Wu, Zhiwu Huang, Dinesh Acharya +4
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is…
Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach
Yuhua Chen, Wen Li, Xiaoran Chen +1
Recently, increasing attention has been drawn to training semantic segmentation models using synthetic data and computer-generated annotation. However, domain gap remains a major b…