106 citations · 377 across the 79 of their papers we have counts for
10 papers · 1 filter
Subspace Adversarial Training
Tao Li, Yingwen Wu, Sizhe Chen +2
Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust. However, a serious problem of catastrophic overfitting exists, i.e.,…
Semi-tensor Product-based TensorDecomposition for Neural Network Compression
Hengling Zhao, Yipeng Liu, Xiaolin Huang +1
The existing tensor networks adopt conventional matrix product for connection. The classical matrix product requires strict dimensionality consistency between factors, which can re…
A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing
Wei Liu, Pingping Zhang, Yinjie Lei +3
Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among diff…
Adaptive Feature Alignment for Adversarial Training
Tao Wang, Ruixin Zhang, Xingyu Chen +6
Recent studies reveal that Convolutional Neural Networks (CNNs) are typically vulnerable to adversarial attacks, which pose a threat to security-sensitive applications. Many advers…
Dominant Patterns: Critical Features Hidden in Deep Neural Networks
Zhixing Ye, Shaofei Qin, Sizhe Chen +1
In this paper, we find the existence of critical features hidden in Deep NeuralNetworks (DNNs), which are imperceptible but can actually dominate the outputof DNNs. We call these f…
Residual Enhanced Multi-Hypergraph Neural Network
Jing Huang, Xiaolin Huang, Jie Yang
Hypergraphs are a generalized data structure of graphs to model higher-order correlations among entities, which have been successfully adopted into various research domains. Meanwh…
Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernels
Qin Luo, Kun Fang, Jie Yang +1
Random Fourier Features (RFF) demonstrate wellappreciated performance in kernel approximation for largescale situations but restrict kernels to be stationary and positive definite.…
Weighted Neural Tangent Kernel: A Generalized and Improved Network-Induced Kernel
Lei Tan, Shutong Wu, Xiaolin Huang
The Neural Tangent Kernel (NTK) has recently attracted intense study, as it describes the evolution of an over-parameterized Neural Network (NN) trained by gradient descent. Howeve…
Low Dimensional Landscape Hypothesis is True: DNNs can be Trained in Tiny Subspaces
Tao Li, Lei Tan, Qinghua Tao +2
Deep neural networks (DNNs) usually contain massive parameters, but there is redundancy such that it is guessed that the DNNs could be trained in low-dimensional subspaces. In this…
Measuring the Transferability of Attacks by the Norm
Sizhe Chen, Qinghua Tao, Zhixing Ye +1
Deep neural networks could be fooled by adversarial examples with trivial differences to original samples. To keep the difference imperceptible in human eyes, researchers bound the…