37 citations · 108 across the 13 of their papers we have counts for
5 papers · 1 filter
Frequency Regularization for Improving Adversarial Robustness
Binxiao Huang, Chaofan Tao, Rui Lin +1
Deep neural networks are incredibly vulnerable to crafted, human-imperceptible adversarial perturbations. Although adversarial training (AT) has proven to be an effective defense a…
PECAN: A Product-Quantized Content Addressable Memory Network
Jie Ran, Rui Lin, Jason Chun Lok Li +2
A novel deep neural network (DNN) architecture is proposed wherein the filtering and linear transform are realized solely with product quantization (PQ). This results in a natural…
Coarse to Fine: Image Restoration Boosted by Multi-Scale Low-Rank Tensor Completion
Rui Lin, Cong Chen, Ngai Wong
Existing low-rank tensor completion (LRTC) approaches aim at restoring a partially observed tensor by imposing a global low-rank constraint on the underlying completed tensor. Howe…
Deformable Butterfly: A Highly Structured and Sparse Linear Transform
Rui Lin, Jie Ran, King Hung Chiu +2
We introduce a new kind of linear transform named Deformable Butterfly (DeBut) that generalizes the conventional butterfly matrices and can be adapted to various input-output dimen…
What Do Adversarially trained Neural Networks Focus: A Fourier Domain-based Study
Binxiao Huang, Chaofan Tao, Rui Lin +1
Although many fields have witnessed the superior performance brought about by deep learning, the robustness of neural networks remains an open issue. Specifically, a small adversar…