2.4k citations · 3.5k across the 43 of their papers we have counts for
8 papers · 1 filter
Defense-VAE: A Fast and Accurate Defense against Adversarial Attacks
Xiang Li, Shihao Ji
Deep neural networks (DNNs) have been enormously successful across a variety of prediction tasks. However, recent research shows that DNNs are particularly vulnerable to adversaria…
Triple Attention Mixed Link Network for Single Image Super Resolution
Xi Cheng, Xiang Li, Jian Yang
Single image super resolution is of great importance as a low-level computer vision task. Recent approaches with deep convolutional neural networks have achieved im-pressive perfor…
Shape Robust Text Detection with Progressive Scale Expansion Network
Xiang Li, Wenhai Wang, Wenbo Hou +3
The challenges of shape robust text detection lie in two aspects: 1) most existing quadrangular bounding box based detectors are difficult to locate texts with arbitrary shapes, wh…
Adversarial Metric Learning
Shuo Chen, Chen Gong, Jian Yang +3
In the past decades, intensive efforts have been put to design various loss functions and metric forms for metric learning problem. These improvements have shown promising results…
Mixed Link Networks
Wenhai Wang, Xiang Li, Jian Yang +1
Basing on the analysis by revealing the equivalence of modern networks, we find that both ResNet and DenseNet are essentially derived from the same "dense topology", yet they only…
Densely Connected Bidirectional LSTM with Applications to Sentence Classification
Zixiang Ding, Rui Xia, Jianfei Yu +2
Deep neural networks have recently been shown to achieve highly competitive performance in many computer vision tasks due to their abilities of exploring in a much larger hypothesi…
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks
Xi Cheng, Xiang Li, Ying Tai +1
Single image super resolution is a very important computer vision task, with a wide range of applications. In recent years, the depth of the super-resolution model has been constan…
Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift
Xiang Li, Shuo Chen, Xiaolin Hu +1
This paper first answers the question "why do the two most powerful techniques Dropout and Batch Normalization (BN) often lead to a worse performance when they are combined togethe…