Mengyao Zhai

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 565 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 126 citations into it; #13,052 of 21,575 by trust here, #8,296 by citations
• Artificial Intelligence — 74 citations into it; #20,993 of 35,345 by trust here, #11,921 by citations
• Generative Models — 43 citations into it; #1,202 of 1,956 by trust here, #932 by citations
• Representation & Self-Supervised Learning — 21 citations into it
• Efficiency & Compression — 15 citations into it
• Meta & Continual Learning — 14 citations into it; #666 of 1,041 by trust here, #405 by citations
• Optimization & Training — 13 citations into it
• Reinforcement Learning — 9 citations into it

Whom Mengyao Zhai trusts overall

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

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

Who trusts Mengyao Zhai overall

1. Fei Ye
2. Adrian G. Bors
3. Pei-Hung Chen
4. Greg Mori
5. Frederick Tung
6. Martin Mundt
7. Lei Chen
8. Rui Gao
9. Anbang Yao
10. Xiaoyu Tao

Papers from Mengyao Zhai's perspective

Ordered by how strongly the researchers Mengyao Zhai 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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

• Lifelong GAN: Continual Learning for Conditional Image Generation (2019) — 210 citations
• Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation (2021) — 46 citations
• Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation (2021) — 32 citations
• Adaptive Appearance Rendering (2021)