David Stutz

Computer Vision and Pattern Recognition · 2014–2021

A researcher in the Science corpus: 6 papers; trusts 1,274 researchers and is trusted by 1,311 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 204 citations into it; #1,123 of 21,575 by trust here, #5,287 by citations
• Artificial Intelligence — 203 citations into it; #25,706 of 35,345 by trust here, #22,463 by citations
• Safety, Robustness & Interpretability — 139 citations into it; #1,805 of 2,251 by trust here, #1,907 by citations
• Signal Processing — 36 citations into it
• Aerospace Engineering — 34 citations into it
• Optimization & Training — 30 citations into it
• Model Architectures — 26 citations into it
• Generative Models — 23 citations into it

Whom David Stutz trusts overall

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

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

Who trusts David Stutz overall

1. Matthijs Douze
2. Giorgos Tolias
3. Artem Babenko
4. Victor S. Lempitsky
5. Liang Zheng
6. Filip Radenovic
7. Gabriella Csurka
8. Albert Gordo
9. Herv'e J'egou
10. Yaroslav Ganin

Papers from David Stutz's perspective

Ordered by how strongly the researchers David Stutz 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Neural Codes for Image Retrieval (2015) — 876 citations
• Learning 3D Shape Completion from Laser Scan Data with Weak Supervision (2018) — 254 citations
• Disentangling Adversarial Robustness and Generalization (2018) — 329 citations
• Relating Adversarially Robust Generalization to Flat Minima (2021) — 80 citations
• Superpixels: An evaluation of the state-of-the-art (2016) — 520 citations
• Understanding Convolutional Neural Networks (2014) — 174 citations