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20172022
most citedSkip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets

177 citations · 611 across the 18 of their papers we have counts for

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Showing cs.LGShow all

9 papers · 1 filter

cs.LG20226 cited

Transferable Unlearnable Examples

Jie Ren, Han Xu, Yuxuan Wan +3

With more people publishing their personal data online, unauthorized data usage has become a serious concern. The unlearnable strategies have been introduced to prevent third parti…

cs.LG202146 cited

Unlearnable Examples: Making Personal Data Unexploitable

Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani +2

The volume of "free" data on the internet has been key to the current success of deep learning. However, it also raises privacy concerns about the unauthorized exploitation of pers…

cs.LG2021139 cited

Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks

Yige Li, Xixiang Lyu, Nodens Koren +3

Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time attack that injects a trigger pattern into a small proportion of training data so as to contro…

cs.LG2021

Neural Architecture Search via Combinatorial Multi-Armed Bandit

Hanxun Huang, Xingjun Ma, Sarah M. Erfani +1

Neural Architecture Search (NAS) has gained significant popularity as an effective tool for designing high performance deep neural networks (DNNs). NAS can be performed via policy…

cs.LG2020125 cited

Normalized Loss Functions for Deep Learning with Noisy Labels

Xingjun Ma, Hanxun Huang, Yisen Wang +3

Robust loss functions are essential for training accurate deep neural networks (DNNs) in the presence of noisy (incorrect) labels. It has been shown that the commonly used Cross En…

cs.LG2020177 cited

Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets

Dongxian Wu, Yisen Wang, Shu-Tao Xia +2

Skip connections are an essential component of current state-of-the-art deep neural networks (DNNs) such as ResNet, WideResNet, DenseNet, and ResNeXt. Despite their huge success in…