194 citations · 456 across the 8 of their papers we have counts for
8 papers
Joint Adversarial Training: Incorporating both Spatial and Pixel Attacks
Haichao Zhang, Jianyu Wang
Conventional adversarial training methods using attacks that manipulate the pixel value directly and individually, leading to models that are less robust in face of spatial transfo…
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training
Haichao Zhang, Jianyu Wang
We introduce a feature scattering-based adversarial training approach for improving model robustness against adversarial attacks. Conventional adversarial training approaches lever…
Zero-Shot Transfer VQA Dataset
Yuanpeng Li, Yi Yang, Jianyu Wang +1
Acquiring a large vocabulary is an important aspect of human intelligence. Onecommon approach for human to populating vocabulary is to learn words duringreading or listening, and t…
Adversarial Attacks and Defences Competition
Alexey Kurakin, Ian Goodfellow, Samy Bengio +20
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop…
Mitigating Adversarial Effects Through Randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang +2
Convolutional neural networks have demonstrated high accuracy on various tasks in recent years. However, they are extremely vulnerable to adversarial examples. For example, imperce…
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion
Zhishuai Zhang, Cihang Xie, Jianyu Wang +2
In this paper, we study the task of detecting semantic parts of an object, e.g., a wheel of a car, under partial occlusion. We propose that all models should be trained without see…