Shunta Saito

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 2 papers; trusts 797 researchers and is trusted by 73 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 84 citations into it; #3,534 of 21,575 by trust here, #6,795 by citations
• Generative Models — 51 citations into it; #510 of 1,956 by trust here, #748 by citations
• Artificial Intelligence — 39 citations into it
• Model Architectures — 14 citations into it
• Optimization & Training — 9 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Computer Graphics and Computer-Aided Design — 6 citations into it

Whom Shunta Saito trusts overall

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

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

Who trusts Shunta Saito overall

1. Emmanuel Kahembwe
2. Aidan Clark
3. Zhangyang Gao
4. Aliaksandr Siarohin
5. Subramanian Ramamoorthy
6. Jeff Donahue
7. Sergey Tulyakov
8. Songwei Ge
9. Longlong Jing
10. Cheng Tan

Papers from Shunta Saito's perspective

Ordered by how strongly the researchers Shunta Saito trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
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

• Temporal Generative Adversarial Nets with Singular Value Clipping (2016) — 509 citations
• TGANv2: Efficient Training of Large Models for Video Generation with Multiple Subsampling Layers (2018) — 46 citations