Guangrun Wang

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 6 papers; trusts 879 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 216 citations into it; #7,501 of 21,575 by trust here, #5,333 by citations
• Artificial Intelligence — 154 citations into it; #25,182 of 35,345 by trust here, #12,083 by citations
• Model Architectures — 58 citations into it; #1,827 of 2,278 by trust here, #2,078 by citations
• Efficiency & Compression — 39 citations into it
• Representation & Self-Supervised Learning — 33 citations into it
• Neural Architecture Search & AutoML — 29 citations into it
• Optimization & Training — 22 citations into it
• Biomedical Engineering — 9 citations into it; #2,457 of 10,734 by trust here, #4,052 by citations

Whom Guangrun Wang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Yoshua Bengio
6. Yann LeCun
7. Diederik P. Kingma
8. Ilya Sutskever
9. Karen Simonyan
10. Christian Szegedy

Who trusts Guangrun Wang overall

1. Songtao Liu
2. Liang Zheng
3. Miao Zhang
4. Tong Xiao
5. Chi Su
6. Yizeng Han
7. Di Huang
8. Alexander Hermans
9. Liang-Chieh Chen
10. Zhedong Zheng

Papers from Guangrun Wang's perspective

Ordered by how strongly the researchers Guangrun Wang trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Deep feature learning with relative distance comparison for person re-identification (2015) — 686 citations
• Deep Dual Learning for Semantic Image Segmentation (2017) — 96 citations
• BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search (2021) — 124 citations
• Adaptively Connected Neural Networks (2019) — 55 citations
• Block-Wisely Supervised Neural Architecture Search With Knowledge Distillation (2019) — 198 citations
• Dynamic Slimmable Network (2021) — 186 citations