114 citations · 475 across the 42 of their papers we have counts for
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stat.ML2018
Adversarial Example Decomposition
Horace He, Aaron Lou, Qingxuan Jiang +3
Research has shown that widely used deep neural networks are vulnerable to carefully crafted adversarial perturbations. Moreover, these adversarial perturbations often transfer acr…
cs.LG2018★ 4 cited
Intermediate Level Adversarial Attack for Enhanced Transferability
Qian Huang, Zeqi Gu, Isay Katsman +5
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models. Adversarial examples often exhibit black-box transfer, meaning that adversa…
cs.CV2018★ 22 cited
DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation
Zuxuan Wu, Xintong Han, Yen-Liang Lin +4
Harvesting dense pixel-level annotations to train deep neural networks for semantic segmentation is extremely expensive and unwieldy at scale. While learning from synthetic data wh…