24 citations · 66 across the 10 of their papers we have counts for
17 papers
Investigating Top- White-Box and Transferable Black-box Attack
Chaoning Zhang, Philipp Benz, Adil Karjauv +3
Existing works have identified the limitation of top- attack success rate (ASR) as a metric to evaluate the attack strength but exclusively investigated it in the white-box sett…
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo
Chaoning Zhang, Kang Zhang, Trung X. Pham +4
Contrastive learning (CL) is widely known to require many negative samples, 65536 in MoCo for instance, for which the performance of a dictionary-free framework is often inferior b…
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning
Chaoning Zhang, Kang Zhang, Chenshuang Zhang +3
To avoid collapse in self-supervised learning (SSL), a contrastive loss is widely used but often requires a large number of negative samples. Without negative samples yet achieving…
Unrestricted Adversarial Attacks on ImageNet Competition
Yuefeng Chen, Xiaofeng Mao, Yuan He +34
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input. However in the real-wo…
Restoration of Video Frames from a Single Blurred Image with Motion Understanding
Dawit Mureja Argaw, Junsik Kim, Francois Rameau +2
We propose a novel framework to generate clean video frames from a single motion-blurred image. While a broad range of literature focuses on recovering a single image from a blurre…
Universal Adversarial Training with Class-Wise Perturbations
Philipp Benz, Chaoning Zhang, Adil Karjauv +1
Despite their overwhelming success on a wide range of applications, convolutional neural networks (CNNs) are widely recognized to be vulnerable to adversarial examples. This intrig…