466 citations · 2.6k across the 60 of their papers we have counts for
5 papers · 1 filter
Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation
Zhongqi Yue, Hanwang Zhang, Qianru Sun
Domain Adaptation (DA) is always challenged by the spurious correlation between domain-invariant features (e.g., class identity) and domain-specific features (e.g., environment) th…
Interventional Few-Shot Learning
Zhongqi Yue, Hanwang Zhang, Qianru Sun +1
We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning (FSL) methods: the pre-trained knowledge is indeed a confounder that limits the performance. This findi…
Cross-GCN: Enhancing Graph Convolutional Network with -Order Feature Interactions
Fuli Feng, Xiangnan He, Hanwang Zhang +1
Graph Convolutional Network (GCN) is an emerging technique that performs learning and reasoning on graph data. It operates feature learning on the graph structure, through aggregat…
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
Hang Gao, Zheng Shou, Alireza Zareian +2
Deep neural networks suffer from over-fitting and catastrophic forgetting when trained with small data. One natural remedy for this problem is data augmentation, which has been rec…
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
Jun Xiao, Hao Ye, Xiangnan He +3
Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiven…