Yandong Wen

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 7 papers; trusts 976 researchers and is trusted by 395 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 263 citations into it; #2,084 of 21,575 by trust here, #1,353 by citations
• Signal Processing — 85 citations into it; #373 of 7,572 by trust here, #196 by citations
• Artificial Intelligence — 76 citations into it; #13,275 of 35,345 by trust here, #8,210 by citations
• Representation & Self-Supervised Learning — 50 citations into it; #843 of 3,145 by trust here, #693 by citations
• Model Architectures — 34 citations into it; #975 of 2,278 by trust here, #789 by citations
• Evaluation & Benchmarks — 29 citations into it
• Optimization & Training — 12 citations into it; #1,272 of 2,601 by trust here, #759 by citations
• Media Technology — 10 citations into it

Whom Yandong Wen trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Yann LeCun
4. Geoffrey E. Hinton
5. Yi Sun
6. Karen Simonyan
7. Yoshua Bengio
8. David G. Lowe
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Yandong Wen overall

1. Weiyang Liu
2. Jiankang Deng
3. Mei Wang
4. Feng Wang
5. Weihong Deng
6. Rajeev Ranjan
7. Xi Yin
8. Xiang Wu
9. Stefanos P. Zafeiriou
10. Xiao Zhang

Papers from Yandong Wen's perspective

Ordered by how strongly the researchers Yandong Wen 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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Discriminative Feature Learning Approach for Deep Face Recognition (2016) — 4,029 citations
• SphereFace: Deep Hypersphere Embedding for Face Recognition (2017) — 3,098 citations
• Large-Margin Softmax Loss for Convolutional Neural Networks (2016) — 1,593 citations
• Range Loss for Deep Face Recognition with Long-Tailed Training Data (2016) — 472 citations
• Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition (2016) — 171 citations
• SphereFace Revived: Unifying Hyperspherical Face Recognition (2021) — 60 citations
• A Comprehensive Study on Center Loss for Deep Face Recognition (2019) — 96 citations