29 citations · 99 across the 15 of their papers we have counts for
17 papers
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…
VDTR: Video Deblurring with Transformer
Mingdeng Cao, Yanbo Fan, Yong Zhang +2
Video deblurring is still an unsolved problem due to the challenging spatio-temporal modeling process. While existing convolutional neural network-based methods show a limited capa…
StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN
Fei Yin, Yong Zhang, Xiaodong Cun +7
One-shot talking face generation aims at synthesizing a high-quality talking face video from an arbitrary portrait image, driven by a video or an audio segment. One challenging qua…
LAS-AT: Adversarial Training with Learnable Attack Strategy
Xiaojun Jia, Yong Zhang, Baoyuan Wu +3
Adversarial training (AT) is always formulated as a minimax problem, of which the performance depends on the inner optimization that involves the generation of adversarial examples…
E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models
Mohammad Akbari, Amin Banitalebi-Dehkordi, Yong Zhang
Building huge and highly capable language models has been a trend in the past years. Despite their great performance, they incur high computational cost. A common solution is to ap…
Model Composition: Can Multiple Neural Networks Be Combined into a Single Network Using Only Unlabeled Data?
Amin Banitalebi-Dehkordi, Xinyu Kang, Yong Zhang
The diversity of deep learning applications, datasets, and neural network architectures necessitates a careful selection of the architecture and data that match best to a target ap…