Yihao Liu

Computer Vision and Pattern Recognition · with Dong · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 384 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 102 citations into it; #7,184 of 21,575 by trust here, #4,372 by citations
• Media Technology — 53 citations into it
• Generative Models — 27 citations into it; #1,096 of 1,956 by trust here, #535 by citations
• Artificial Intelligence — 17 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 6 citations into it

Whom Yihao Liu trusts overall

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

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

Who trusts Yihao Liu overall

1. Andreas Lugmayr
2. Kai Zhang
3. Radu Timofte
4. Jinjin Gu
5. Manuel Fritsche
6. Jingyun Liang
7. Luc Van Gool
8. Martin Danelljan
9. Fuzhi Yang
10. Yong Guo

Papers from Yihao Liu's perspective

Ordered by how strongly the researchers Yihao Liu 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks (2018) — 4,766 citations
• RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-Resolution (2019) — 409 citations
• FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing (2020) — 321 citations