33 citations · 178 across the 28 of their papers we have counts for
6 papers · 1 filter
Generative Dynamic Patch Attack
Xiang Li, Shihao Ji
Adversarial patch attack is a family of attack algorithms that perturb a part of image to fool a deep neural network model. Existing patch attacks mostly consider injecting adversa…
JEM++: Improved Techniques for Training JEM
Xiulong Yang, Shihao Ji
Joint Energy-based Model (JEM) is a recently proposed hybrid model that retains strong discriminative power of modern CNN classifiers, while generating samples rivaling the quality…
Improving Text-to-Image Synthesis Using Contrastive Learning
Hui Ye, Xiulong Yang, Martin Takac +2
The goal of text-to-image synthesis is to generate a visually realistic image that matches a given text description. In practice, the captions annotated by humans for the same imag…
Dep-: Improving -based Network Sparsification via Dependency Modeling
Yang Li, Shihao Ji
Training deep neural networks with an regularization is one of the prominent approaches for network pruning or sparsification. The method prunes the network during training b…
Reducing Risk and Uncertainty of Deep Neural Networks on Diagnosing COVID-19 Infection
Krishanu Sarker, Sharbani Pandit, Anupam Sarker +2
Effective and reliable screening of patients via Computer-Aided Diagnosis can play a crucial part in the battle against COVID-19. Most of the existing works focus on developing sop…
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More
Xiulong Yang, Hui Ye, Yang Ye +2
Joint Energy-based Model (JEM) of Grathwohl et al. shows that a standard softmax classifier can be reinterpreted as an energy-based model (EBM) for the joint distribution p(x,y); t…