21 citations · 173 across the 53 of their papers we have counts for
7 papers · 1 filter
Adversarial Examples on Segmentation Models Can be Easy to Transfer
Jindong Gu, Hengshuang Zhao, Volker Tresp +1
Deep neural network-based image classification can be misled by adversarial examples with small and quasi-imperceptible perturbations. Furthermore, the adversarial examples created…
Are Vision Transformers Robust to Patch Perturbations?
Jindong Gu, Volker Tresp, Yao Qin
Recent advances in Vision Transformer (ViT) have demonstrated its impressive performance in image classification, which makes it a promising alternative to Convolutional Neural Net…
Simple Distillation Baselines for Improving Small Self-supervised Models
Jindong Gu, Wei Liu, Yonglong Tian
While large self-supervised models have rivalled the performance of their supervised counterparts, small models still struggle. In this report, we explore simple baselines for impr…
Attacking Adversarial Attacks as A Defense
Boxi Wu, Heng Pan, Li Shen +6
It is well known that adversarial attacks can fool deep neural networks with imperceptible perturbations. Although adversarial training significantly improves model robustness, fai…
Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning
Zhiliang Wu, Yinchong Yang, Jindong Gu +1
Deep neural networks are increasingly being used for the analysis of medical images. However, most works neglect the uncertainty in the model's prediction. We propose an uncertaint…
Capsule Network is Not More Robust than Convolutional Network
Jindong Gu, Volker Tresp, Han Hu
The Capsule Network is widely believed to be more robust than Convolutional Networks. However, there are no comprehensive comparisons between these two networks, and it is also unk…
Effective and Efficient Vote Attack on Capsule Networks
Jindong Gu, Baoyuan Wu, Volker Tresp
Standard Convolutional Neural Networks (CNNs) can be easily fooled by images with small quasi-imperceptible artificial perturbations. As alternatives to CNNs, the recently proposed…