Michal Geyer

Computer Vision and Pattern Recognition · 2022–2023

A researcher in the Science corpus: 2 papers; trusts 177 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 76 citations into it; #18,723 of 21,575 by trust here, #12,419 by citations
• Generative Models — 49 citations into it

Whom Michal Geyer trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Jonathan Ho
5. Kaiming He
6. Yoshua Bengio
7. Ilya Sutskever
8. Yann LeCun
9. Alec Radford
10. Jia Deng

Who trusts Michal Geyer overall

1. Omer Bar-Tal
2. D. Epstein
3. Shaoteng Liu
4. Elad Richardson
5. Guangxuan Xiao

Papers from Michal Geyer's perspective

Ordered by how strongly the researchers Michal Geyer trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation (2022) — 1,092 citations
• TokenFlow: Consistent Diffusion Features for Consistent Video Editing (2023) — 490 citations