Chenshen Wu

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 171 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 95 citations into it; #7,936 of 21,575 by trust here, #4,555 by citations
• Artificial Intelligence — 58 citations into it; #11,914 of 35,345 by trust here, #6,193 by citations
• Generative Models — 54 citations into it; #756 of 1,956 by trust here, #501 by citations
• Representation & Self-Supervised Learning — 29 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Optimization & Training — 11 citations into it
• Biophysics — 8 citations into it
• Cognitive Neuroscience — 6 citations into it

Whom Chenshen Wu trusts overall

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

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

Who trusts Chenshen Wu overall

1. Fei Ye
2. Timothée Lesort
3. Adrian G. Bors
4. Subhankar Ghosh
5. Hugo Caselles-Dupré
6. Oleksiy Ostapenko
7. Liyuan Wang
8. Tyler L. Hayes
9. Xialei Liu
10. Mengyao Zhai

Papers from Chenshen Wu's perspective

Ordered by how strongly the researchers Chenshen Wu 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Transferring GANs: generating images from limited data (2018) — 320 citations
• Memory Replay GANs: learning to generate images from new categories without forgetting (2018) — 203 citations
• Generative Feature Replay For Class-Incremental Learning (2020) — 190 citations