Philipp Fischer

Computer Vision and Pattern Recognition · 2014–2016

A researcher in the Science corpus: 6 papers; trusts 450 researchers and is trusted by 9,070 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 119 citations into it; #214 of 21,575 by trust here, #175 by citations
• Artificial Intelligence — 35 citations into it; #12,763 of 35,345 by trust here, #8,810 by citations
• Aerospace Engineering — 20 citations into it
• Representation & Self-Supervised Learning — 17 citations into it; #1,557 of 3,145 by trust here, #1,159 by citations
• Media Technology — 12 citations into it; #6 of 3,621 by trust here, #5 by citations
• Optimization & Training — 9 citations into it
• Biophysics — 5 citations into it; #4 of 1,196 by trust here, #3 by citations

Whom Philipp Fischer trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. David G. Lowe
4. Geoffrey E. Hinton
5. Ross B. Girshick
6. Diederik P. Kingma
7. Krystian Mikolajczyk
8. Yoshua Bengio
9. Ilya Sutskever
10. Kaiming He

Who trusts Philipp Fischer overall

1. Luc Van Gool
2. Andrea Vedaldi
3. Kristen Grauman
4. Liang-Chieh Chen
5. Vladlen Koltun
6. Yoshua Bengio
7. Jitendra Malik
8. Dacheng Tao
9. Andrew Zisserman
10. Trevor Darrell

Papers from Philipp Fischer's perspective

Ordered by how strongly the researchers Philipp Fischer 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• U-Net: Convolutional Networks for Biomedical Image Segmentation (2015) — 98,692 citations
• FlowNet: Learning Optical Flow with Convolutional Networks (2015) — 4,664 citations
• A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation (2015) — 3,047 citations
• Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks (2014) — 636 citations
• Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT (2014) — 279 citations
• A benchmark for comparison of dental radiography analysis algorithms (2016) — 380 citations