32 citations · 52 across the 15 of their papers we have counts for
5 papers · 1 filter
CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue
Keke Tang, Dingruibo Miao, Weilong Peng +5
Overconfident predictions on out-of-distribution (OOD) samples is a thorny issue for deep neural networks. The key to resolve the OOD overconfidence issue inherently is to build a…
Visually Imperceptible Adversarial Patch Attacks on Digital Images
Yaguan Qian, Jiamin Wang, Bin Wang +4
The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted more attention. Many algorithms have been proposed to craft powerful adversarial examples. Ho…
Spot Evasion Attacks: Adversarial Examples for License Plate Recognition Systems with Convolutional Neural Networks
Ya-guan Qian, Dan-feng Ma, Bin Wang +5
Recent studies have shown convolution neural networks (CNNs) for image recognition are vulnerable to evasion attacks with carefully manipulated adversarial examples. Previous work…
Decision Propagation Networks for Image Classification
Keke Tang, Peng Song, Yuexin Ma +4
High-level (e.g., semantic) features encoded in the latter layers of convolutional neural networks are extensively exploited for image classification, leaving low-level (e.g., colo…
Attending Category Disentangled Global Context for Image Classification
Keke Tang, Guodong Wei, Runnan Chen +3
In this paper, we propose a general framework for image classification using the attention mechanism and global context, which could incorporate with various network architectures…