Miaoyun Zhao

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 3 papers; trusts 803 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 160 citations into it
• Artificial Intelligence — 121 citations into it
• Generative Models — 80 citations into it; #1,017 of 1,956 by trust here, #1,221 by citations
• Representation & Self-Supervised Learning — 29 citations into it
• Natural Language Processing — 15 citations into it
• Radiology, Nuclear Medicine and Imaging — 14 citations into it
• Signal Processing — 14 citations into it
• Meta & Continual Learning — 13 citations into it

Whom Miaoyun Zhao trusts overall

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

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

Who trusts Miaoyun Zhao overall

1. Yulai Cong
2. Juwon Seo
3. Liyuan Wang
4. Nupur Kumari
5. Jingwen Kang
6. Xingxing Zhang
7. Jianqiao Li
8. Bingliang Zhang
9. Pei-Hung Chen
10. Gyeong-Moon Park

Papers from Miaoyun Zhao's perspective

Ordered by how strongly the researchers Miaoyun Zhao 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• GAN Memory with No Forgetting (2020) — 151 citations
• On Leveraging Pretrained GANs for Generation with Limited Data (2020) — 102 citations
• On Leveraging Pretrained GANs for Limited-Data Generation (2020) — 22 citations