Thomas Schlegl

Computer Vision and Pattern Recognition · 2014–2019

A researcher in the Science corpus: 3 papers; trusts 38 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 26 citations into it
• Artificial Intelligence — 23 citations into it; #9,065 of 35,345 by trust here, #5,682 by citations
• Generative Models — 12 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Optimization & Training — 5 citations into it

Whom Thomas Schlegl trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Honglak Lee
4. Diederik P. Kingma
5. Dan Claudiu Ciresan
6. Yoshua Bengio
7. Yann LeCun
8. Andrew Y. Ng
9. Ilya Sutskever
10. J. Urgen Schmidhuber

Who trusts Thomas Schlegl overall

1. Paul Bergmann
2. Kilian Batzner
3. Vitjan Zavrtanik
4. Lukas Ruff
5. Denis A. Gudovskiy
6. Guansong Pang
7. Jakob Bozic
8. Christoph Baur
9. Guodong Wang
10. Julian Wyatt

Papers from Thomas Schlegl's perspective

Ordered by how strongly the researchers Thomas Schlegl 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery (2017) — 2,527 citations
• f‐AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks (2019) — 1,462 citations
• Unsupervised Pre-training Across Image Domains Improves Lung Tissue Classification (2014) — 69 citations