Claudio Michaelis

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 835 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 252 citations into it; #5,573 of 21,575 by trust here, #2,915 by citations
• Artificial Intelligence — 170 citations into it
• Evaluation & Benchmarks — 47 citations into it
• Representation & Self-Supervised Learning — 35 citations into it
• Model Architectures — 22 citations into it
• Cognitive Neuroscience — 20 citations into it; #5,892 of 10,852 by trust here, #824 by citations
• Natural Language Processing — 20 citations into it
• Meta & Continual Learning — 17 citations into it

Whom Claudio Michaelis trusts overall

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

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

Who trusts Claudio Michaelis overall

1. Evgenia Rusak
2. Robert Geirhos
3. Dan Andrei Ganea
4. Jake Snell
5. Steffen Schneider
6. Lukas Schott
7. Zhangjing Wang
8. Shahaf E. Finder
9. Axel Sauer
10. Zhibo Fan

Papers from Claudio Michaelis's perspective

Ordered by how strongly the researchers Claudio Michaelis 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

• ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness (2018) — 3,222 citations
• Shortcut learning in deep neural networks (2020) — 3,155 citations
• Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming (2019) — 574 citations
• One-shot Texture Segmentation (2018) — 18 citations
• One-Shot Instance Segmentation (2018) — 99 citations