11 citations · 44 across the 10 of their papers we have counts for
6 papers · 1 filter
Geometry-Aware Unsupervised Domain Adaptation
You-Wei Luo, Chuan-Xian Ren, Zi-Ying Chen
Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain in the presence of dataset shift. Most existing me…
Graph Neural Reasoning May Fail in Certifying Boolean Unsatisfiability
Ziliang Chen, Zhanfu Yang
It is feasible and practically-valuable to bridge the characteristics between graph neural networks (GNNs) and logical reasoning. Despite considerable efforts and successes witness…
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
Ziliang Chen, Zhanfu Yang, Xiaoxi Wang +4
A broad range of cross--domain generation researches boil down to matching a joint distribution by deep generative models (DGMs). Hitherto algorithms excel in pairwise domains w…
Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks
Ziliang Chen, Jingyu Zhuang, Xiaodan Liang +1
(Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training. Learning domain-in…
FRAME Revisited: An Interpretation View Based on Particle Evolution
Xu Cai, Yang Wu, Guanbin Li +2
FRAME (Filters, Random fields, And Maximum Entropy) is an energy-based descriptive model that synthesizes visual realism by capturing mutual patterns from structural input signals.…
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
Ruijia Xu, Ziliang Chen, Wangmeng Zuo +2
Unsupervised domain adaptation (UDA) conventionally assumes labeled source samples coming from a single underlying source distribution. Whereas in practical scenario, labeled data…