453 citations · 1.5k across the 44 of their papers we have counts for
12 papers · 1 filter
Parameter Interpolation Adversarial Training for Robust Image Classification
Xin Liu, Yichen Yang, Kun He +1
Though deep neural networks exhibit superior performance on various tasks, they are still plagued by adversarial examples. Adversarial training has been demonstrated to be the most…
PIAT: Parameter Interpolation based Adversarial Training for Image Classification
Kun He, Xin Liu, Yichen Yang +4
Adversarial training has been demonstrated to be the most effective approach to defend against adversarial attacks. However, existing adversarial training methods show apparent osc…
Class-aware Information for Logit-based Knowledge Distillation
Shuoxi Zhang, Hanpeng Liu, John E. Hopcroft +1
Knowledge distillation aims to transfer knowledge to the student model by utilizing the predictions/features of the teacher model, and feature-based distillation has recently shown…
Local Magnification for Data and Feature Augmentation
Kun He, Chang Liu, Stephen Lin +1
In recent years, many data augmentation techniques have been proposed to increase the diversity of input data and reduce the risk of overfitting on deep neural networks. In this wo…
Integrating Large Circular Kernels into CNNs through Neural Architecture Search
Kun He, Chao Li, Yixiao Yang +2
The square kernel is a standard unit for contemporary CNNs, as it fits well on the tensor computation for convolution operation. However, the retinal ganglion cells in the biologic…
Single Image Reflection Removal through Cascaded Refinement
Chao Li, Yixiao Yang, Kun He +2
We address the problem of removing undesirable reflections from a single image captured through a glass surface, which is an ill-posed, challenging but practically important proble…
Robust Local Features for Improving the Generalization of Adversarial Training
Chuanbiao Song, Kun He, Jiadong Lin +2
Adversarial training has been demonstrated as one of the most effective methods for training robust models to defend against adversarial examples. However, adversarially trained mo…
AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples
Xiaosen Wang, Kun He, Chuanbiao Song +2
Despite the rapid development of adversarial machine learning, most adversarial attack and defense researches mainly focus on the perturbation-based adversarial examples, which is…
The Local Dimension of Deep Manifold
Mengxiao Zhang, Wangquan Wu, Yanren Zhang +4
Based on our observation that there exists a dramatic drop for the singular values of the fully connected layers or a single feature map of the convolutional layer, and that the di…
Randomness in Deconvolutional Networks for Visual Representation
Kun He, Jingbo Wang, Haochuan Li +5
Toward a deeper understanding on the inner work of deep neural networks, we investigate CNN (convolutional neural network) using DCN (deconvolutional network) and randomization tec…
Stacked Generative Adversarial Networks
Xun Huang, Yixuan Li, Omid Poursaeed +2
In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up…
A Powerful Generative Model Using Random Weights for the Deep Image Representation
Kun He, Yan Wang, John Hopcroft
To what extent is the success of deep visualization due to the training? Could we do deep visualization using untrained, random weight networks? To address this issue, we explore n…