11 citations · 15 across the 3 of their papers we have counts for
6 papers · 1 filter
Multi-stage Deep Classifier Cascades for Open World Recognition
Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu +4
At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase. However, real-world problem is far more…
Interpreting and Evaluating Neural Network Robustness
Fuxun Yu, Zhuwei Qin, Chenchen Liu +3
Recently, adversarial deception becomes one of the most considerable threats to deep neural networks. However, compared to extensive research in new designs of various adversarial…
Progressive Weight Pruning of Deep Neural Networks using ADMM
Shaokai Ye, Tianyun Zhang, Kaiqi Zhang +10
Deep neural networks (DNNs) although achieving human-level performance in many domains, have very large model size that hinders their broader applications on edge computing devices…
Interpreting Adversarial Robustness: A View from Decision Surface in Input Space
Fuxun Yu, Chenchen Liu, Yanzhi Wang +2
One popular hypothesis of neural network generalization is that the flat local minima of loss surface in parameter space leads to good generalization. However, we demonstrate that…
Functionality-Oriented Convolutional Filter Pruning
Zhuwei Qin, Fuxun Yu, Chenchen Liu +1
The sophisticated structure of Convolutional Neural Network (CNN) allows for outstanding performance, but at the cost of intensive computation. As significant redundancies inevitab…
Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients
Fuxun Yu, Zirui Xu, Yanzhi Wang +2
In recent years, neural networks have demonstrated outstanding effectiveness in a large amount of applications.However, recent works have shown that neural networks are susceptible…