13 citations · 69 across the 12 of their papers we have counts for
17 papers
Reliable Evaluation of Adversarial Transferability
Wenqian Yu, Jindong Gu, Zhijiang Li +1
Adversarial examples (AEs) with small adversarial perturbations can mislead deep neural networks (DNNs) into wrong predictions. The AEs created on one DNN can also fool another DNN…
CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering
Yao Zhang, Haokun Chen, Ahmed Frikha +5
Visual Question Answering (VQA) is a multi-discipline research task. To produce the right answer, it requires an understanding of the visual content of images, the natural language…
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…