activity
20172020
most citedOnline PCB Defect Detector On A New PCB Defect Dataset

79 citations · 176 across the 10 of their papers we have counts for

collaborators

11 papers

cs.LG2020

Sparse Generalized Canonical Correlation Analysis: Distributed Alternating Iteration based Approach

Jia Cai, Kexin Lv, Junyi Huo +2

Sparse canonical correlation analysis (CCA) is a useful statistical tool to detect latent information with sparse structures. However, sparse CCA works only for two datasets, i.e.,…

cs.CV20204 cited

Double Backpropagation for Training Autoencoders against Adversarial Attack

Chengjin Sun, Sizhe Chen, Xiaolin Huang

Deep learning, as widely known, is vulnerable to adversarial samples. This paper focuses on the adversarial attack on autoencoders. Safety of the autoencoders (AEs) is important be…

cs.CV2020

Type I Attack for Generative Models

Chengjin Sun, Sizhe Chen, Jia Cai +1

Generative models are popular tools with a wide range of applications. Nevertheless, it is as vulnerable to adversarial samples as classifiers. The existing attack methods mainly f…

cs.CV20195 cited

Mixed-Precision Quantized Neural Network with Progressively Decreasing Bitwidth For Image Classification and Object Detection

Tianshu Chu, Qin Luo, Jie Yang +1

Efficient model inference is an important and practical issue in the deployment of deep neural network on resource constraint platforms. Network quantization addresses this problem…

cs.LG20197 cited

DAmageNet: A Universal Adversarial Dataset

Sizhe Chen, Xiaolin Huang, Zhengbao He +1

It is now well known that deep neural networks (DNNs) are vulnerable to adversarial attack. Adversarial samples are similar to the clean ones, but are able to cheat the attacked DN…

cs.LG2019

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

Fanghui Liu, Xiaolin Huang, Yudong Chen +2

In this paper, we propose a fast surrogate leverage weighted sampling strategy to generate refined random Fourier features for kernel approximation. Compared to the current state-o…