8 citations · 20 across the 6 of their papers we have counts for
9 papers
Training Deep Neural Networks via Branch-and-Bound
Yuanwei Wu, Ziming Zhang, Guanghui Wang
In this paper, we propose BPGrad, a novel approximate algorithm for deep nueral network training, based on adaptive estimates of feasible region via branch-and-bound. The method is…
Self-Orthogonality Module: A Network Architecture Plug-in for Learning Orthogonal Filters
Ziming Zhang, Wenchi Ma, Yuanwei Wu +1
In this paper, we investigate the empirical impact of orthogonality regularization (OR) in deep learning, either solo or collaboratively. Recent works on OR showed some promising r…
MDFN: Multi-Scale Deep Feature Learning Network for Object Detection
Wenchi Ma, Yuanwei Wu, Feng Cen +1
This paper proposes an innovative object detector by leveraging deep features learned in high-level layers. Compared with features produced in earlier layers, the deep features are…
Object Detection with Convolutional Neural Networks
Kaidong Li, Wenchi Ma, Usman Sajid +2
In this chapter, we present a brief overview of the recent development in object detection using convolutional neural networks (CNN). Several classical CNN-based detectors are pres…
Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization
Wenju Xu, Yuanwei Wu, Wenchi Ma +1
In this paper, we address the problem of weakly supervised object localization (WSL), which trains a detection network on the dataset with only image-level annotations. The propose…
Unsupervised Deep Feature Transfer for Low Resolution Image Classification
Yuanwei Wu, Ziming Zhang, Guanghui Wang
In this paper, we propose a simple while effective unsupervised deep feature transfer algorithm for low resolution image classification. No fine-tuning on convenet filters is requi…