Yen-Yu Lin

Computer Vision and Pattern Recognition · 2005–2022

A researcher in the Science corpus: 5 papers; trusts 908 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 153 citations into it; #7,537 of 21,575 by trust here, #7,176 by citations
• Artificial Intelligence — 58 citations into it; #13,581 of 35,345 by trust here, #11,921 by citations
• Model Architectures — 20 citations into it
• Media Technology — 18 citations into it; #2,121 of 3,621 by trust here, #2,706 by citations
• Generative Models — 16 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Aerospace Engineering — 13 citations into it
• Evaluation & Benchmarks — 10 citations into it

Whom Yen-Yu Lin trusts overall

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

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

Who trusts Yen-Yu Lin overall

1. Deng Cai
2. Xiaofei He
3. Ritendra Datta
4. Jiawei Han
5. Wilhelm Tranheden
6. Lin Wang
7. Jingkuan Song
8. Hsin-Ying Lee
9. Viktor Olsson
10. Dhiraj Joshi

Papers from Yen-Yu Lin's perspective

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

Their papers

• Adversarial Learning for Semi-supervised Semantic Segmentation (2018) — 619 citations
• CrDoCo: Pixel-Level Domain Transfer With Cross-Domain Consistency (2019) — 300 citations
• Semantic manifold learning for image retrieval (2005) — 117 citations
• Stripformer: Strip Transformer for Fast Image Deblurring (2022) — 323 citations
• FSA-Net: Learning Fine-Grained Structure Aggregation for Head Pose Estimation From a Single Image (2019) — 299 citations