Andreas Lugmayr

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 5 papers; trusts 651 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 196 citations into it; #10,393 of 21,575 by trust here, #5,843 by citations
• Media Technology — 94 citations into it
• Generative Models — 79 citations into it; #1,099 of 1,956 by trust here, #1,181 by citations
• Evaluation & Benchmarks — 23 citations into it
• Artificial Intelligence — 18 citations into it
• Computational Mechanics — 12 citations into it
• Statistical and Nonlinear Physics — 12 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it

Whom Andreas Lugmayr trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Ross B. Girshick
9. Karen Simonyan
10. Radu Timofte

Who trusts Andreas Lugmayr overall

1. Kai Zhang
2. Radu Timofte
3. Honggang Chen
4. Liangbin Xie
5. Manuel Fritsche
6. Zhen-Wen Han
7. Jingyun Liang
8. Juan Miguel Lopez Alcaraz
9. Yufei Wang
10. Ziwei Luo

Papers from Andreas Lugmayr's perspective

Ordered by how strongly the researchers Andreas Lugmayr 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. GENERATIVE ADVERSARIAL NETS

Their papers

• RePaint: Inpainting using Denoising Diffusion Probabilistic Models (2022) — 2,174 citations
• SRFlow: Learning the Super-Resolution Space with Normalizing Flow (2020) — 425 citations
• NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results (2020) — 190 citations
• Unsupervised Learning for Real-World Super-Resolution (2019) — 182 citations
• Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (2021) — 125 citations