Jian Chen

Computer Vision and Pattern Recognition · with Guo · 2016–2020

A researcher in the Science corpus: 6 papers; trusts 1,339 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 358 citations into it; #13,828 of 21,575 by trust here, #8,296 by citations
• Artificial Intelligence — 140 citations into it; #32,234 of 35,345 by trust here, #18,586 by citations
• Media Technology — 112 citations into it; #2,700 of 3,621 by trust here, #2,114 by citations
• Model Architectures — 70 citations into it
• Generative Models — 41 citations into it
• Evaluation & Benchmarks — 30 citations into it
• Optimization & Training — 30 citations into it
• Efficiency & Compression — 29 citations into it

Whom Jian Chen trusts overall

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

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

Who trusts Jian Chen overall

1. Yong Guo
2. Syed Waqas Zamir
3. Chaofeng Wang
4. Honggang Chen
5. Wei Tang
6. Hengyuan Zhao
7. Aditya Arora
8. Jingyun Liang

Papers from Jian Chen's perspective

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

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

Their papers

• Deep Learning for Image Super-Resolution: A Survey (2019) — 1,821 citations
• NAT: Neural Architecture Transformer for Accurate and Compact Architectures (2019) — 101 citations
• Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search (2020) — 78 citations
• Hierarchical Neural Architecture Search for Single Image Super-Resolution (2020) — 53 citations
• Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution (2020) — 319 citations
• The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs (2016) — 13 citations