8 citations · 8 across the 4 of their papers we have counts for
5 papers
Reconciling Attribute and Structural Anomalies for Improved Graph Anomaly Detection
Chunjing Xiao, Jiahui Lu, Xovee Xu +4
Graph anomaly detection is critical in domains such as healthcare and economics, where identifying deviations can prevent substantial losses. Existing unsupervised approaches striv…
Feature Mining: A Novel Training Strategy for Convolutional Neural Network
Tianshu Xie, Xuan Cheng, Xiaomin Wang +3
In this paper, we propose a novel training strategy for convolutional neural network(CNN) named Feature Mining, that aims to strengthen the network's learning of the local feature.…
Go Small and Similar: A Simple Output Decay Brings Better Performance
Xuan Cheng, Tianshu Xie, Xiaomin Wang +3
Regularization and data augmentation methods have been widely used and become increasingly indispensable in deep learning training. Researchers who devote themselves to this have c…
FocusedDropout for Convolutional Neural Network
Tianshu Xie, Minghui Liu, Jiali Deng +3
In convolutional neural network (CNN), dropout cannot work well because dropped information is not entirely obscured in convolutional layers where features are correlated spatially…
Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural Network
Tianshu Xie, Xuan Cheng, Minghui Liu +3
In this paper, we propose a novel data augmentation strategy named Cut-Thumbnail, that aims to improve the shape bias of the network. We reduce an image to a certain size and repla…