Ying Fu

Computer Vision and Pattern Recognition · 2 papers · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 401 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 51 citations into it; #9,648 of 21,575 by trust here, #8,722 by citations
• Generative Models — 30 citations into it; #903 of 1,956 by trust here, #1,010 by citations
• Artificial Intelligence — 26 citations into it; #14,972 of 35,345 by trust here, #12,761 by citations
• Meta & Continual Learning — 10 citations into it; #455 of 1,041 by trust here, #443 by citations
• Model Architectures — 8 citations into it
• Optimization & Training — 8 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Ying Fu trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Karen Simonyan
5. Yoshua Bengio
6. Diederik P. Kingma
7. Tero Karras
8. Yann LeCun
9. Christian Szegedy
10. Andrew Zisserman

Who trusts Ying Fu overall

1. Jiangpeng He
2. Hongjoon Ahn
3. Fei Zhu
4. Feng Zhu
5. Ali Ayub
6. Da-Wei Zhou
7. Xialei Liu
8. Julien Pourcel
9. Zhiwei Lin
10. Youngmin Oh

Papers from Ying Fu's perspective

Ordered by how strongly the researchers Ying Fu 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Incremental Learning Using Conditional Adversarial Networks (2019) — 193 citations
• Disentangled Face Attribute Editing via Instance-Aware Latent Space Search (2021) — 30 citations