126 citations · 472 across the 48 of their papers we have counts for
10 papers · 1 filter
Cooperative Learning for Noisy Supervision
Hao Wu, Jiangchao Yao, Ya Zhang +1
Learning with noisy labels has gained the enormous interest in the robust deep learning area. Recent studies have empirically disclosed that utilizing dual networks can enhance the…
Collaborative Label Correction via Entropy Thresholding
Hao Wu, Jiangchao Yao, Jiajie Wang +3
Deep neural networks (DNNs) have the capacity to fit extremely noisy labels nonetheless they tend to learn data with clean labels first and then memorize those with noisy labels. W…
Spatio-Temporal Sparsification for General Robust Graph Convolution Networks
Mingming Lu, Ya Zhang
Graph Neural Networks (GNNs) have attracted increasing attention due to its successful applications on various graph-structure data. However, recent studies have shown that adversa…
ESAD: End-to-end Deep Semi-supervised Anomaly Detection
Chaoqin Huang, Fei Ye, Peisen Zhao +3
This paper explores semi-supervised anomaly detection, a more practical setting for anomaly detection where a small additional set of labeled samples are provided. We propose a new…
Learning on Attribute-Missing Graphs
Xu Chen, Siheng Chen, Jiangchao Yao +3
Graphs with complete node attributes have been widely explored recently. While in practice, there is a graph where attributes of only partial nodes could be available and those of…
Sampling and Recovery of Graph Signals based on Graph Neural Networks
Siheng Chen, Maosen Li, Ya Zhang
We propose interpretable graph neural networks for sampling and recovery of graph signals, respectively. To take informative measurements, we propose a new graph neural sampling mo…
Graph Cross Networks with Vertex Infomax Pooling
Maosen Li, Siheng Chen, Ya Zhang +1
We propose a novel graph cross network (GXN) to achieve comprehensive feature learning from multiple scales of a graph. Based on trainable hierarchical representations of a graph,…
Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data
Zhuoxun He, Lingxi Xie, Xin Chen +3
Data augmentation has been widely applied as an effective methodology to improve generalization in particular when training deep neural networks. Recently, researchers proposed a f…
Defending Adversarial Attacks by Correcting logits
Yifeng Li, Lingxi Xie, Ya Zhang +3
Generating and eliminating adversarial examples has been an intriguing topic in the field of deep learning. While previous research verified that adversarial attacks are often frag…
Accelerate CNN via Recursive Bayesian Pruning
Yuefu Zhou, Ya Zhang, Yanfeng Wang +1
Channel Pruning, widely used for accelerating Convolutional Neural Networks, is an NP-hard problem due to the inter-layer dependency of channel redundancy. Existing methods general…