Edgar Schönfeld

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 613 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 119 citations into it; #10,309 of 21,575 by trust here, #7,176 by citations
• Artificial Intelligence — 71 citations into it; #33,275 of 35,345 by trust here, #14,718 by citations
• Generative Models — 69 citations into it; #1,012 of 1,956 by trust here, #1,287 by citations
• Representation & Self-Supervised Learning — 28 citations into it; #2,952 of 3,145 by trust here, #1,843 by citations
• Radiology, Nuclear Medicine and Imaging — 20 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Optimization & Training — 13 citations into it
• Media Technology — 12 citations into it

Whom Edgar Schönfeld trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Kaiming He
6. Ilya Sutskever
7. Yann LeCun
8. Jimmy Ba
9. Karen Simonyan
10. Ruslan Salakhutdinov

Who trusts Edgar Schönfeld overall

1. Shafin Rahman
2. Ali Cheraghian
3. Shiyu Chang
4. Prateek Katiyar
5. Liangbin Xie
6. Peirong Ma
7. Han-Jia Ye
8. Samarth Sinha
9. Kun-Juan Wei
10. S. Divakar Bhat

Papers from Edgar Schönfeld's perspective

Ordered by how strongly the researchers Edgar Schönfeld 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

• A U-Net Based Discriminator for Generative Adversarial Networks (2020) — 348 citations
• Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders (2018) — 693 citations
• You Only Need Adversarial Supervision for Semantic Image Synthesis (2020) — 221 citations
• Generalized Zero-Shot Learning via Aligned Variational Autoencoders (2019) — 24 citations