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20172025
most citedSkip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets

177 citations · 611 across the 24 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

cs.LG2019

Symmetric Cross Entropy for Robust Learning with Noisy Labels

Yisen Wang, Xingjun Ma, Zaiyi Chen +3

Training accurate deep neural networks (DNNs) in the presence of noisy labels is an important and challenging task. Though a number of approaches have been proposed for learning wi…

cs.CV2019

Generative Image Inpainting with Submanifold Alignment

Ang Li, Jianzhong Qi, Rui Zhang +2

Image inpainting aims at restoring missing regions of corrupted images, which has many applications such as image restoration and object removal. However, current GAN-based generat…

cs.CR2019

Towards Fair and Privacy-Preserving Federated Deep Models

Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar +5

The current standalone deep learning framework tends to result in overfitting and low utility. This problem can be addressed by either a centralized framework that deploys a centra…

cs.LG20195 cited

Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality

Sukarna Barua, Xingjun Ma, Sarah Monazam Erfani +2

Generative Adversarial Networks (GANs) are an elegant mechanism for data generation. However, a key challenge when using GANs is how to best measure their ability to generate reali…

cs.LG20199 cited

Black-box Adversarial Attacks on Video Recognition Models

Linxi Jiang, Xingjun Ma, Shaoxiang Chen +2

Deep neural networks (DNNs) are known for their vulnerability to adversarial examples. These are examples that have undergone small, carefully crafted perturbations, and which can…