Gustav Larsson

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 722 researchers and is trusted by 2,175 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 92 citations into it; #844 of 21,575 by trust here, #2,055 by citations
• Artificial Intelligence — 50 citations into it; #2,988 of 35,345 by trust here, #4,316 by citations
• Model Architectures — 29 citations into it; #489 of 2,278 by trust here, #645 by citations
• Optimization & Training — 18 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Generative Models — 10 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Media Technology — 6 citations into it

Whom Gustav Larsson trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Karen Simonyan
6. Diederik P. Kingma
7. Ross B. Girshick
8. Yann LeCun
9. Sergey Ioffe
10. Christian Szegedy

Who trusts Gustav Larsson overall

1. Richard Zhang
2. Spyros Gidaris
3. Longlong Jing
4. Phillip Isola
5. Alexei A. Efros
6. Jun-Yan Zhu
7. Gao Huang
8. Andrea Vedaldi
9. Guo-Jun Qi
10. Dacheng Tao

Papers from Gustav Larsson's perspective

Ordered by how strongly the researchers Gustav Larsson 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

• Learning Representations for Automatic Colorization (2016) — 1,061 citations
• FractalNet: Ultra-Deep Neural Networks without Residuals (2016) — 1,061 citations
• Colorization as a Proxy Task for Visual Understanding (2017) — 520 citations