Dongyoon Han
Computer Vision and Pattern Recognition · 2016–2021
A researcher in the Science corpus: 7 papers; trusts 953 researchers and is trusted by 745 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 232 citations into it; #1,606 of 21,575 by trust here, #1,338 by citations
- Artificial Intelligence — 160 citations into it; #2,393 of 35,345 by trust here, #2,091 by citations
- Model Architectures — 59 citations into it; #397 of 2,278 by trust here, #762 by citations
- Optimization & Training — 32 citations into it; #886 of 2,601 by trust here, #295 by citations
- Evaluation & Benchmarks — 30 citations into it
- Media Technology — 30 citations into it
- Natural Language Processing — 19 citations into it
- Efficiency & Compression — 16 citations into it
Whom Dongyoon Han trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Karen Simonyan
- Christian Szegedy
- Ross B. Girshick
- Yann LeCun
- Ilya Sutskever
- Yoshua Bengio
- Diederik P. Kingma
Who trusts Dongyoon Han overall
- Sangdoo Yun
- Byeongho Heo
- Han Cai
- Ryo Takahashi
- Hadi Pouransari
- Ekin Dogus Cubuk
- Zhiqiang Shen
- George Philipp
- Nitish Shirish Keskar
- Oren Nuriel
Papers from Dongyoon Han's perspective
Ordered by how strongly the researchers Dongyoon Han trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features (2019) — 6,079 citations
- Deep Pyramidal Residual Networks (2016) — 759 citations
- Rethinking Spatial Dimensions of Vision Transformers (2021) — 757 citations
- Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels (2021) — 176 citations
- Character Region Awareness for Text Detection (2019) — 980 citations
- What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis (2019) — 570 citations
- OCR-Free Document Understanding Transformer (2021) — 538 citations