Leyuan Fang

Media Technology · 2012–2019

A researcher in the Science corpus: 12 papers; trusts 376 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Media Technology — 235 citations into it; #653 of 3,621 by trust here, #339 by citations
• Computer Vision and Pattern Recognition — 230 citations into it; #8,644 of 21,575 by trust here, #9,187 by citations
• Atmospheric Science — 65 citations into it; #3,688 of 4,804 by trust here, #2,311 by citations
• Artificial Intelligence — 44 citations into it
• Computational Mechanics — 40 citations into it
• Analytical Chemistry — 26 citations into it
• Biomedical Engineering — 22 citations into it; #7,759 of 10,734 by trust here, #7,661 by citations
• Model Architectures — 18 citations into it

Whom Leyuan Fang trusts overall

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

1. Shutao Li
2. Jon Atli Benediktsson
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. José M. Bioucas-Dias
6. Chih-Jen Lin
7. Farid Melgani
8. Kaiming He
9. Yoshua Bengio
10. Yuliya Tarabalka

Who trusts Leyuan Fang overall

1. Shutao Li
2. Renwei Dian
3. Yao Wang
4. Lin He
5. Jiayi Ma
6. Qiqi Zhu
7. Rui Li
8. Jiangjun Peng
9. Weiwei Song
10. Wei Tang

Papers from Leyuan Fang's perspective

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

Their papers

• Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion (2012) — 364 citations
• Deep Learning for Hyperspectral Image Classification: An Overview (2019) — 1,595 citations
• Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution (2019) — 373 citations
• Hyperspectral Image Classification With Deep Feature Fusion Network (2018) — 450 citations
• Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization (2018) — 527 citations
• Deep Hyperspectral Image Sharpening (2018) — 360 citations
• Pixel-level image fusion: A survey of the state of the art (2017) — 1,186 citations
• Hyperspectral Image Super-Resolution via Non-local Sparse Tensor Factorization (2017) — 298 citations
• Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. (2017) — 460 citations
• Classification of Hyperspectral Images by Exploiting Spectral–Spatial Information of Superpixel via Multiple Kernels (2015) — 354 citations
• Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation (2014) — 300 citations
• Remote Sensing Image Fusion via Sparse Representations Over Learned Dictionaries (2013) — 296 citations