Christian Riess

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 6 papers; trusts 497 researchers and is trusted by 61 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 186 citations into it; #3,667 of 21,575 by trust here, #3,156 by citations
• Artificial Intelligence — 77 citations into it; #33,235 of 35,345 by trust here, #13,962 by citations
• Generative Models — 37 citations into it; #1,187 of 1,956 by trust here, #1,091 by citations
• Radiology, Nuclear Medicine and Imaging — 26 citations into it
• Model Architectures — 19 citations into it
• Signal Processing — 19 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Safety, Robustness & Interpretability — 17 citations into it; #1,067 of 2,251 by trust here, #1,404 by citations

Whom Christian Riess trusts overall

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

1. Diederik P. Kingma
2. Davide Cozzolino
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Kaiming He
7. Yann LeCun
8. Justus Thies
9. Karen Simonyan
10. Ilya Sutskever

Who trusts Christian Riess overall

1. Luisa Verdoliva
2. Thanh Thi Nguyen
3. Yuezun Li
4. Rubén Tolosana
5. Nils Hulzebosch
6. Justus Thies
7. Iacopo Masi
8. Hasam Khalid
9. Huy Hoang Le Nguyen
10. Umur Aybars Ciftci

Papers from Christian Riess's perspective

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

Their papers

• FaceForensics++: Learning to Detect Manipulated Facial Images (2019) — 3,187 citations
• FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces (2018) — 452 citations
• ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection (2018) — 321 citations
• Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations (2019) — 751 citations
• Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images (2018) — 470 citations
• A Gentle Introduction to Deep Learning in Medical Image Processing (2018) — 467 citations