16 citations · 47 across the 11 of their papers we have counts for
7 papers · 1 filter
PokeBNN: A Binary Pursuit of Lightweight Accuracy
Yichi Zhang, Zhiru Zhang, Lukasz Lew
Optimization of Top-1 ImageNet promotes enormous networks that may be impractical in inference settings. Binary neural networks (BNNs) have the potential to significantly lower the…
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…
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework
Lifan Yuan, Yichi Zhang, Yangyi Chen +1
Despite recent success on various tasks, deep learning techniques still perform poorly on adversarial examples with small perturbations. While optimization-based methods for advers…
Adversarial Semantic Contour for Object Detection
Yichi Zhang, Zijian Zhu, Xiao Yang +1
Modern object detectors are vulnerable to adversarial examples, which brings potential risks to numerous applications, e.g., self-driving car. Among attacks regularized by …
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining
Weizhe Hua, Yichi Zhang, Chuan Guo +2
Neural network robustness has become a central topic in machine learning in recent years. Most training algorithms that improve the model's robustness to adversarial and common cor…
U-Net-and-a-half: Convolutional network for biomedical image segmentation using multiple expert-driven annotations
Yichi Zhang, Jesper Kers, Clarissa A. Cassol +8
Development of deep learning systems for biomedical segmentation often requires access to expert-driven, manually annotated datasets. If more than a single expert is involved in th…
Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation
Zhe Xie, Chengxuan Liu, Yichi Zhang +3
Sequential recommendation as an emerging topic has attracted increasing attention due to its important practical significance. Models based on deep learning and attention mechanism…