50 citations · 136 across the 10 of their papers we have counts for
10 papers
Asymptotic Statistical Analysis of -divergence GAN
Xinwei Shen, Kani Chen, Tong Zhang
Generative Adversarial Networks (GANs) have achieved great success in data generation. However, its statistical properties are not fully understood. In this paper, we consider the…
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions
Jiafan He, Dongruo Zhou, Tong Zhang +1
We study the linear contextual bandit problem in the presence of adversarial corruption, where the reward at each round is corrupted by an adversary, and the corruption level (i.e.…
Black-box Prompt Learning for Pre-trained Language Models
Shizhe Diao, Zhichao Huang, Ruijia Xu +4
The increasing scale of general-purpose Pre-trained Language Models (PLMs) necessitates the study of more efficient adaptation across different downstream tasks. In this paper, we…
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training
Chen Liu, Zhichao Huang, Mathieu Salzmann +2
Adversarial training is a popular method to robustify models against adversarial attacks. However, it exhibits much more severe overfitting than training on clean inputs. In this w…
Optimizing Latent Space Directions For GAN-based Local Image Editing
Ehsan Pajouheshgar, Tong Zhang, Sabine Süsstrunk
Generative Adversarial Network (GAN) based localized image editing can suffer from ambiguity between semantic attributes. We thus present a novel objective function to evaluate the…
Modeling Object Dissimilarity for Deep Saliency Prediction
Bahar Aydemir, Deblina Bhattacharjee, Tong Zhang +3
Saliency prediction has made great strides over the past two decades, with current techniques modeling low-level information, such as color, intensity and size contrasts, and high-…
Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling
Zhichao Huang, Xintong Han, Jia Xu +1
We present a new method for few-shot human motion transfer that achieves realistic human image generation with only a small number of appearance inputs. Despite recent advances in…
CorrAttack: Black-box Adversarial Attack with Structured Search
Zhichao Huang, Yaowei Huang, Tong Zhang
We present a new method for score-based adversarial attack, where the attacker queries the loss-oracle of the target model. Our method employs a parameterized search space with a s…
Black-Box Adversarial Attack with Transferable Model-based Embedding
Zhichao Huang, Tong Zhang
We present a new method for black-box adversarial attack. Unlike previous methods that combined transfer-based and scored-based methods by using the gradient or initialization of a…
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Yandong Li, Lijun Li, Liqiang Wang +2
Powerful adversarial attack methods are vital for understanding how to construct robust deep neural networks (DNNs) and for thoroughly testing defense techniques. In this paper, we…