Johannes Totz

Computer Vision and Pattern Recognition · 2016

A researcher in the Science corpus: 3 papers; trusts 348 researchers and is trusted by 522 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 140 citations into it; #1,871 of 21,575 by trust here, #731 by citations
• Media Technology — 61 citations into it; #221 of 3,621 by trust here, #16 by citations
• Artificial Intelligence — 24 citations into it
• Generative Models — 19 citations into it; #608 of 1,956 by trust here, #116 by citations
• Computational Mechanics — 14 citations into it
• Model Architectures — 12 citations into it
• Optimization & Training — 8 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Johannes Totz trusts overall

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

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

Who trusts Johannes Totz overall

1. Yulun Zhang
2. Kai Zhang
3. Radu Timofte
4. Xintao Wang
5. Chao Dong
6. Yapeng Tian
7. Wei-Sheng Lai
8. Yong Guo
9. Simon Niklaus
10. Xin Deng

Papers from Johannes Totz's perspective

Ordered by how strongly the researchers Johannes Totz 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. ImageNet Large Scale Visual Recognition Challenge
8. Attention is All you Need (Neural Information Processing Systems)
9. Going deeper with convolutions
10. Long Short-Term Memory

Their papers

• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (2016) — 12,248 citations
• Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (2016) — 6,030 citations
• Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation (2016) — 740 citations