activity
20172022
most citedDeep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning

11 citations · 44 across the 8 of their papers we have counts for

collaborators

11 papers

cs.CV20228 cited

Open Set Domain Adaptation By Novel Class Discovery

Jingyu Zhuang, Ziliang Chen, Pengxu Wei +2

In Open Set Domain Adaptation (OSDA), large amounts of target samples are drawn from the implicit categories that never appear in the source domain. Due to the lack of their specif…

cs.CL20208 cited

Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation

Shuai Lin, Pan Zhou, Xiaodan Liang +4

Human doctors with well-structured medical knowledge can diagnose a disease merely via a few conversations with patients about symptoms. In contrast, existing knowledge-grounded di…

cs.LG20196 cited

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…

cs.CV2019

Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning

Xiaopeng Yan, Ziliang Chen, Anni Xu +3

Resembling the rapid learning capability of human, few-shot learning empowers vision systems to understand new concepts by training with few samples. Leading approaches derived fro…

cs.LG20192 cited

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…

cs.LG2019

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…