21 citations · 38 across the 5 of their papers we have counts for
8 papers
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free
Haotao Wang, Tianlong Chen, Shupeng Gui +3
Adversarial training and its many variants substantially improve deep network robustness, yet at the cost of compromising standard accuracy. Moreover, the training process is heavy…
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
Haotao Wang, Shupeng Gui, Haichuan Yang +2
Generative adversarial networks (GANs) have gained increasing popularity in various computer vision applications, and recently start to be deployed to resource-constrained mobile d…
Hierarchical Prototype Learning for Zero-Shot Recognition
Xingxing Zhang, Shupeng Gui, Zhenfeng Zhu +2
Zero-Shot Learning (ZSL) has received extensive attention and successes in recent years especially in areas of fine-grained object recognition, retrieval, and image captioning. Key…
ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries
Xingxing Zhang, Shupeng Gui, Zhenfeng Zhu +2
Zero-shot learning (ZSL) has received extensive attention recently especially in areas of fine-grained object recognition, retrieval, and image captioning. Due to the complete lack…
Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based Approach
Haichuan Yang, Shupeng Gui, Yuhao Zhu +1
Deep Neural Networks (DNNs) are applied in a wide range of usecases. There is an increased demand for deploying DNNs on devices that do not have abundant resources such as memory a…
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions
Shupeng Gui, Xiangliang Zhang, Pan Zhong +5
Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recomm…