Christian Richardt

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 564 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 206 citations into it; #4,199 of 21,575 by trust here, #3,680 by citations
• Generative Models — 74 citations into it
• Artificial Intelligence — 39 citations into it
• Computational Mechanics — 39 citations into it; #5,040 of 10,001 by trust here, #1,745 by citations
• Computer Graphics and Computer-Aided Design — 32 citations into it; #442 of 1,584 by trust here, #329 by citations
• Signal Processing — 16 citations into it
• Model Architectures — 12 citations into it
• Optimization & Training — 12 citations into it

Whom Christian Richardt trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Christian Szegedy
8. Ilya Sutskever
9. Individualized Treat
10. Ross B. Girshick

Who trusts Christian Richardt overall

1. Michael Niemeyer
2. Peng Wang
3. Eric Chan
4. Garrett Wilson
5. Zhipeng Bao
6. Zekun Hao
7. Luke Melas-Kyriazi
8. Jiatao Gu
9. Lingjie Liu
10. Anpei Chen

Papers from Christian Richardt's perspective

Ordered by how strongly the researchers Christian Richardt 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. ImageNet Large Scale Visual Recognition Challenge
9. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• HoloGAN: Unsupervised Learning of 3D Representations From Natural Images (2019) — 554 citations
• Deep video portraits (2018) — 780 citations
• BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images (2020) — 253 citations
• Unsupervised Attention-guided Image to Image Translation (2018) — 323 citations
• InverseFaceNet: Deep Single-Shot Inverse Face Rendering From A Single Image (2017) — 56 citations
• InverseFaceNet: Deep Monocular Inverse Face Rendering (2017) — 83 citations