44 citations · 88 across the 10 of their papers we have counts for
5 papers · 1 filter
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning
Yunhui Guo, Yandong Li, Liqiang Wang +1
There is an increasing number of pre-trained deep neural network models. However, it is still unclear how to effectively use these models for a new task. Transfer learning, which a…
Defending Against Adversarial Attacks Using Random Forests
Yifan Ding, Liqiang Wang, Huan Zhang +3
As deep neural networks (DNNs) have become increasingly important and popular, the robustness of DNNs is the key to the safety of both the Internet and the physical world. Unfortun…
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness
Adnan Siraj Rakin, Zhezhi He, Li Yang +3
Deep Neural Network (DNN) trained by the gradient descent method is known to be vulnerable to maliciously perturbed adversarial input, aka. adversarial attack. As one of the counte…
Frame-Recurrent Video Inpainting by Robust Optical Flow Inference
Yifan Ding, Chuan Wang, Haibin Huang +3
In this paper, we present a new inpainting framework for recovering missing regions of video frames. Compared with image inpainting, performing this task on video presents new chal…
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Yandong Li, Lijun Li, Liqiang Wang +2
Powerful adversarial attack methods are vital for understanding how to construct robust deep neural networks (DNNs) and for thoroughly testing defense techniques. In this paper, we…