Satoshi Iizuka

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 553 researchers and is trusted by 255 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 107 citations into it; #2,476 of 21,575 by trust here, #3,459 by citations
• Artificial Intelligence — 23 citations into it
• Computer Graphics and Computer-Aided Design — 20 citations into it
• Model Architectures — 14 citations into it
• Media Technology — 13 citations into it; #209 of 3,621 by trust here, #204 by citations
• Optimization & Training — 13 citations into it
• Generative Models — 11 citations into it; #1,398 of 1,956 by trust here, #1,435 by citations
• Computational Mechanics — 8 citations into it

Whom Satoshi Iizuka trusts overall

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

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

Who trusts Satoshi Iizuka overall

1. Bao Gia Doan
2. Yi Wang
3. Longlong Jing
4. Ryohei Suzuki
5. Guilin Liu
6. Anh Tuan Tran
7. Zhe L. Lin
8. Richard Zhang
9. Jiahui Yu
10. Chao Yang

Papers from Satoshi Iizuka's perspective

Ordered by how strongly the researchers Satoshi Iizuka 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Globally and locally consistent image completion (2017) — 2,208 citations
• Let there be color! (2016) — 508 citations
• DeepProp: Extracting Deep Features from a Single Image for Edit Propagation (2016) — 70 citations
• Learning to simplify (2016) — 130 citations