Kuniaki Saito

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 1,160 researchers and is trusted by 153 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 194 citations into it; #4,336 of 21,575 by trust here, #4,584 by citations
• Artificial Intelligence — 168 citations into it; #1,787 of 35,345 by trust here, #2,700 by citations
• Representation & Self-Supervised Learning — 85 citations into it
• Evaluation & Benchmarks — 22 citations into it
• Generative Models — 21 citations into it
• Model Architectures — 20 citations into it
• Optimization & Training — 19 citations into it
• Cancer Research — 16 citations into it; #641 of 1,780 by trust here, #1,486 by citations

Whom Kuniaki Saito trusts overall

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

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

Who trusts Kuniaki Saito overall

1. Jian Liang
2. Garrett Wilson
3. Dapeng Hu
4. Shiqi Yang
5. Mohammad Rostami
6. Marco Toldo
7. Jihan Yang
8. Chaoqi Chen
9. Hui Tang
10. Ruijia Xu

Papers from Kuniaki Saito's perspective

Ordered by how strongly the researchers Kuniaki Saito 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• Adversarial Dropout Regularization (2017) — 296 citations
• Open Set Domain Adaptation by Backpropagation (2018) — 610 citations
• Semi-Supervised Domain Adaptation via Minimax Entropy (2019) — 738 citations
• Strong-Weak Distribution Alignment for Adaptive Object Detection (2018) — 756 citations
• Universal Domain Adaptation through Self Supervision (2020) — 391 citations
• Maximum Classifier Discrepancy for Unsupervised Domain Adaptation (2017) — 2,008 citations
• Asymmetric Tri-training for Unsupervised Domain Adaptation (2017) — 615 citations