Shaohui Lin

Computer Vision and Pattern Recognition · with Zhang · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 528 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it
• Artificial Intelligence — 107 citations into it
• Efficiency & Compression — 89 citations into it
• Model Architectures — 38 citations into it
• Media Technology — 16 citations into it
• Optimization & Training — 16 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Computational Mathematics — 7 citations into it

Whom Shaohui Lin trusts overall

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

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

Who trusts Shaohui Lin overall

1. Ben Mussay
2. Jing Liu
3. Yang He
4. Jie Gui
5. Jiao Xie
6. Abdul Jabbar
7. Sehoon Kim
8. Junjie Liu
9. Zhaohui Yang
10. Dan Feldman

Papers from Shaohui Lin's perspective

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

Their papers

• Towards Optimal Structured CNN Pruning via Generative Adversarial Learning (2019) — 578 citations
• Contrastive Learning for Compact Single Image Dehazing (2021) — 860 citations
• Training convolutional neural networks with cheap convolutions and online distillation (2019) — 15 citations
• Accelerating Convolutional Networks via Global & Dynamic Filter Pruning (2018) — 271 citations