18 citations · 31 across the 7 of their papers we have counts for
7 papers
Improving Your Graph Neural Networks: A High-Frequency Booster
Jiaqi Sun, Lin Zhang, Shenglin Zhao +1
Graph neural networks (GNNs) hold the promise of learning efficient representations of graph-structured data, and one of its most important applications is semi-supervised node cla…
Edge of chaos as a guiding principle for modern neural network training
Lin Zhang, Ling Feng, Kan Chen +1
The success of deep neural networks in real-world problems has prompted many attempts to explain their training dynamics and generalization performance, but more guiding principles…
A One-Shot Texture-Perceiving Generative Adversarial Network for Unsupervised Surface Inspection
Lingyun Gu, Lin Zhang, Zhaokui Wang
Visual surface inspection is a challenging task owing to the highly diverse appearance of target surfaces and defective regions. Previous attempts heavily rely on vast quantities o…
Multi-robot Cooperative Object Transportation using Decentralized Deep Reinforcement Learning
Lin Zhang, Hao Xiong, Ou Ma +1
Object transportation could be a challenging problem for a single robot due to the oversize and/or overweight issues. A multi-robot system can take the advantage of increased drivi…
One-Shot Object Detection without Fine-Tuning
Xiang Li, Lin Zhang, Yau Pun Chen +2
Deep learning has revolutionized object detection thanks to large-scale datasets, but their object categories are still arguably very limited. In this paper, we attempt to enrich s…
Phase Contrast Microscopy Cell PopulationSegmentation: A Survey
Lin Zhang
Phase contrast microscopy (PCM) has been widely used in biomedicine research, which allows users to observe objectives without staining or killing them. One important related resea…
An Efficient Approach for Cell Segmentation in Phase Contrast Microscopy Images
Lin Zhang
In this paper, we propose a new model to segment cells in phase contrast microscopy images. Cell images collected from the similar scenario share a similar background. Inspired by…