Yanfeng Gu

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 3 papers; trusts 261 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 61 citations into it; #6,734 of 21,575 by trust here, #4,926 by citations
• Media Technology — 39 citations into it; #723 of 3,621 by trust here, #326 by citations
• Artificial Intelligence — 15 citations into it
• Atmospheric Science — 15 citations into it; #1,271 of 4,804 by trust here, #537 by citations
• Representation & Self-Supervised Learning — 7 citations into it
• Ecology — 6 citations into it
• Model Architectures — 6 citations into it
• Ocean Engineering — 6 citations into it

Whom Yanfeng Gu trusts overall

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

1. Geoffrey E. Hinton
2. Gong Cheng
3. Anil M. Cheriyadat
4. Alex Krizhevsky
5. Andrew Zisserman
6. Josef Sivic
7. David G. Lowe
8. Karen Simonyan
9. Yoshua Bengio
10. Yann LeCun

Who trusts Yanfeng Gu overall

1. Gong Cheng
2. Qi Wang
3. Junwei Han
4. Andreas Kamilaris
5. Shaoteng Liu

Papers from Yanfeng Gu's perspective

Ordered by how strongly the researchers Yanfeng Gu 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. ImageNet: A large-scale hierarchical image database
4. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Feature Fusion for VHR Remote Sensing Scene Classification (2017) — 419 citations
• An Informative Feature Selection Method Based on Sparse PCA for VHR Scene Classification (2016) — 29 citations
• Deep Learning-Based Classification of Hyperspectral Data (2014) — 2,291 citations