34 citations · 63 across the 11 of their papers we have counts for
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cs.CV2022★ 22 cited
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation
Zeyu Qin, Yanbo Fan, Yi Liu +4
Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples, which can produce erroneous predictions by injecting imperceptible perturbations. In this work…
cs.CV2022★ 3 cited
Few-Shot Adaptation of Pre-Trained Networks for Domain Shift
Wenyu Zhang, Li Shen, Wanyue Zhang +1
Deep networks are prone to performance degradation when there is a domain shift between the source (training) data and target (test) data. Recent test-time adaptation methods updat…
cs.CV2022
Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance
Shiwei Liu, Yuesong Tian, Tianlong Chen +1
Generative adversarial networks (GANs) have received an upsurging interest since being proposed due to the high quality of the generated data. While achieving increasingly impressi…