Mehdi Noroozi

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 926 researchers and is trusted by 4,648 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 124 citations into it; #447 of 21,575 by trust here, #1,326 by citations
• Artificial Intelligence — 67 citations into it; #4,916 of 35,345 by trust here, #6,848 by citations
• Representation & Self-Supervised Learning — 39 citations into it; #97 of 3,145 by trust here, #224 by citations
• Evaluation & Benchmarks — 16 citations into it
• Model Architectures — 10 citations into it
• Optimization & Training — 8 citations into it
• Generative Models — 7 citations into it
• Biophysics — 6 citations into it

Whom Mehdi Noroozi trusts overall

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

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

Who trusts Mehdi Noroozi overall

1. Longlong Jing
2. Ishan Misra
3. Spyros Gidaris
4. Xiaohua Zhai
5. Mathilde Caron
6. Yuki M. Asano
7. Andrea Vedaldi
8. Piotr Bojanowski
9. Alexander Kolesnikov
10. Abhinav Kumar Gupta

Papers from Mehdi Noroozi's perspective

Ordered by how strongly the researchers Mehdi Noroozi 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles (2016) — 3,276 citations
• Representation Learning by Learning to Count (2017) — 379 citations
• Boosting Self-Supervised Learning via Knowledge Transfer (2018) — 320 citations
• 3D CNNs with Adaptive Temporal Feature Resolutions (2020) — 45 citations