Christian Ledig

Computer Vision and Pattern Recognition · 2013–2017

A researcher in the Science corpus: 9 papers; trusts 362 researchers and is trusted by 265 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 182 citations into it; #2,434 of 21,575 by trust here, #776 by citations
• Artificial Intelligence — 55 citations into it; #18,329 of 35,345 by trust here, #4,276 by citations
• Media Technology — 53 citations into it; #539 of 3,621 by trust here, #34 by citations
• Model Architectures — 22 citations into it
• Generative Models — 21 citations into it; #607 of 1,956 by trust here, #116 by citations
• Optimization & Training — 17 citations into it
• Computational Mechanics — 14 citations into it
• Biomedical Engineering — 5 citations into it; #3,952 of 10,734 by trust here, #1,553 by citations

Whom Christian Ledig trusts overall

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

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

Who trusts Christian Ledig overall

1. Radu Timofte
2. Yochai Blau
3. Kai Zhang
4. Yulun Zhang
5. Luc Van Gool
6. Xintao Wang
7. Yongqiang Zhang
8. Roey Mechrez
9. Jingyun Liang
10. He Zhang

Papers from Christian Ledig's perspective

Ordered by how strongly the researchers Christian Ledig 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. ImageNet: A large-scale hierarchical image database
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

• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (2016) — 12,248 citations
• Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation (2016) — 3,360 citations
• Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation (2016) — 740 citations
• Cardiac Image Super-Resolution with Global Correspondence Using Multi-Atlas PatchMatch (2013) — 264 citations
• Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research (2017) — 2,023 citations
• DeepMedic for Brain Tumor Segmentation (2016) — 325 citations
• Is the deconvolution layer the same as a convolutional layer? (2016) — 148 citations
• Unsupervised domain adaptation in brain lesion segmentation with adversarial networks (2016) — 544 citations
• Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain (2014) — 388 citations