Joachim Denzler

Computer Vision and Pattern Recognition · 2014–2019

A researcher in the Science corpus: 8 papers; trusts 1,027 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 168 citations into it; #6,689 of 21,575 by trust here, #7,852 by citations
• Artificial Intelligence — 101 citations into it; #22,395 of 35,345 by trust here, #8,999 by citations
• Evaluation & Benchmarks — 34 citations into it; #2,004 of 3,147 by trust here, #2,627 by citations
• Representation & Self-Supervised Learning — 24 citations into it
• Media Technology — 20 citations into it
• Model Architectures — 20 citations into it; #1,014 of 2,278 by trust here, #1,826 by citations
• Aerospace Engineering — 13 citations into it; #3,666 of 6,421 by trust here, #3,644 by citations
• Safety, Robustness & Interpretability — 11 citations into it; #765 of 2,251 by trust here, #1,437 by citations

Whom Joachim Denzler trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Yann LeCun
6. Andrew Zisserman
7. Ross B. Girshick
8. Ilya Sutskever
9. Karen Simonyan
10. Jia Deng

Who trusts Joachim Denzler overall

1. Quanshi Zhang
2. Marcel Simon
3. Erik Rodner
4. Nikhil Vyas
5. Eduardo Fonseca
6. Andrew Ilyas
7. Alessandro Achille
8. Ruiming Cao
9. Ying Nian Wu

Papers from Joachim Denzler's perspective

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

Their papers

• Nonparametric Part Transfer for Fine-Grained Recognition (2014) — 122 citations
• Do We Train on Test Data? Purging CIFAR of Near-Duplicates (2019) — 116 citations
• Part Detector Discovery in Deep Convolutional Neural Networks (2014) — 76 citations
• Deep learning and process understanding for data-driven Earth system science (2019) — 4,521 citations
• Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches (2016) — 41 citations
• Active learning and discovery of object categories in the presence of unnameable instances (2015) — 58 citations
• Deep Learning on Small Datasets without Pre-Training using Cosine Loss (2019) — 157 citations
• Local Novelty Detection in Multi-class Recognition Problems (2015) — 62 citations