Erik Rodner

Computer Vision and Pattern Recognition · 2013–2016

A researcher in the Science corpus: 9 papers; trusts 1,098 researchers and is trusted by 923 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 189 citations into it; #1,417 of 21,575 by trust here, #3,191 by citations
• Artificial Intelligence — 106 citations into it; #10,932 of 35,345 by trust here, #8,157 by citations
• Representation & Self-Supervised Learning — 42 citations into it; #474 of 3,145 by trust here, #885 by citations
• Evaluation & Benchmarks — 25 citations into it
• Aerospace Engineering — 18 citations into it; #3,667 of 6,421 by trust here, #3,644 by citations
• Media Technology — 14 citations into it
• Safety, Robustness & Interpretability — 11 citations into it; #1,005 of 2,251 by trust here, #1,857 by citations
• Signal Processing — 10 citations into it

Whom Erik Rodner trusts overall

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

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

Who trusts Erik Rodner overall

1. Quanshi Zhang
2. Judy Hoffman
3. Marcel Simon
4. Heliang Zheng
5. Gabriella Csurka
6. Song-Chun Zhu
7. Zhishuai Zhang
8. Ying Nian Wu
9. Tsung-Yu Lin
10. Trevor Darrell

Papers from Erik Rodner's perspective

Ordered by how strongly the researchers Erik Rodner 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. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Efficient Learning of Domain-invariant Image Representations (2013) — 296 citations
• Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks (2015) — 426 citations
• Open-vocabulary Object Retrieval (2014) — 95 citations
• Semi-supervised Domain Adaptation with Instance Constraints (2013) — 190 citations
• Nonparametric Part Transfer for Fine-Grained Recognition (2014) — 122 citations
• Part Detector Discovery in Deep Convolutional Neural Networks (2014) — 76 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
• Local Novelty Detection in Multi-class Recognition Problems (2015) — 62 citations