Chuan Li

Computer Vision and Pattern Recognition · with Wand · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 597 researchers and is trusted by 1,414 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 94 citations into it; #1,089 of 21,575 by trust here, #2,739 by citations
• Generative Models — 51 citations into it; #124 of 1,956 by trust here, #383 by citations
• Computer Graphics and Computer-Aided Design — 28 citations into it; #21 of 1,584 by trust here, #77 by citations
• Artificial Intelligence — 20 citations into it
• Computational Mechanics — 15 citations into it; #5,470 of 10,001 by trust here, #2,112 by citations
• Safety, Robustness & Interpretability — 7 citations into it
• Media Technology — 5 citations into it
• Model Architectures — 5 citations into it

Whom Chuan Li trusts overall

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

1. Karen Simonyan
2. Diederik P. Kingma
3. Alexei A. Efros
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Kaiming He
7. Andrew Zisserman
8. Matthew D. Zeiler
9. Yoshua Bengio
10. Yann LeCun

Who trusts Chuan Li overall

1. Yijun Li
2. Dae Young Park
3. Dongdong Chen
4. Yongcheng Jing
5. Xun Huang
6. Jun-Yan Zhu
7. Roman Novak
8. Yaroslav Nikulin
9. Samaneh Azadi
10. Amir Atapour-Abarghouei

Papers from Chuan Li's perspective

Ordered by how strongly the researchers Chuan Li 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. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
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

• Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks (2016) — 1,522 citations
• HoloGAN: Unsupervised Learning of 3D Representations From Natural Images (2019) — 554 citations
• Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis (2016) — 811 citations