Wilhelm Tranheden

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 2 papers; trusts 357 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 75 citations into it; #13,269 of 21,575 by trust here, #15,162 by citations
• Artificial Intelligence — 53 citations into it; #21,419 of 35,345 by trust here, #23,077 by citations
• Representation & Self-Supervised Learning — 22 citations into it
• Radiology, Nuclear Medicine and Imaging — 14 citations into it; #1,870 of 3,265 by trust here, #2,914 by citations
• Optimization & Training — 13 citations into it
• Evaluation & Benchmarks — 10 citations into it

Whom Wilhelm Tranheden trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Geoff French
5. Yoshua Bengio
6. Diederik P. Kingma
7. Jia Deng
8. Ross B. Girshick
9. Yann LeCun
10. Dong-Hyun Lee

Who trusts Wilhelm Tranheden overall

1. Lukas Hoyer
2. Yahao Liu
3. Dengxin Dai
4. Jinhong Deng
5. Luc Van Gool
6. Yuxi Wang
7. Shikun Liu
8. Massih-Reza Amini

Papers from Wilhelm Tranheden's perspective

Ordered by how strongly the researchers Wilhelm Tranheden trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning (2020) — 420 citations
• DACS: Domain Adaptation via Cross-domain Mixed Sampling (2020) — 458 citations