Yi Zhang

Computer Vision and Pattern Recognition · IEEE Transactions on Medical Imaging · 2017–2018

A researcher in the Science corpus: 6 papers; trusts 75 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 112 citations into it; #21,018 of 21,575 by trust here, #8,122 by citations
• Radiology, Nuclear Medicine and Imaging — 67 citations into it; #1,550 of 3,265 by trust here, #531 by citations
• Artificial Intelligence — 48 citations into it
• Biomedical Engineering — 40 citations into it; #5,162 of 10,734 by trust here, #2,298 by citations
• Generative Models — 38 citations into it
• Media Technology — 37 citations into it
• Optimization & Training — 15 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Yi Zhang trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Kaiming He
4. Jimmy Ba
5. Alex Krizhevsky
6. Karen Simonyan
7. Yann LeCun
8. Yoshua Bengio
9. Ge Wang
10. Ian J. Goodfellow

Who trusts Yi Zhang overall

1. Hongming Shan
2. Kyong Hwan Jin
3. Michael T. McCann
4. Koichiro Yasaka
5. Eunhee Kang
6. Morteza Mardani
7. Qingsong Yang

Papers from Yi Zhang's perspective

Ordered by how strongly the researchers Yi Zhang 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss (2017) — 1,435 citations
• Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network (2017) — 1,621 citations
• Low-dose CT via convolutional neural network. (2017) — 688 citations
• 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network (2018) — 392 citations
• LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT (2017) — 408 citations
• CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) (2018) — 479 citations