Junsuk Choe
Computer Vision and Pattern Recognition · 2019–2021
A researcher in the Science corpus: 4 papers; trusts 1,055 researchers and is trusted by 423 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 169 citations into it; #2,049 of 21,575 by trust here, #1,827 by citations
- Artificial Intelligence — 124 citations into it; #4,531 of 35,345 by trust here, #2,711 by citations
- Model Architectures — 39 citations into it
- Evaluation & Benchmarks — 24 citations into it
- Optimization & Training — 24 citations into it; #884 of 2,601 by trust here, #295 by citations
- Efficiency & Compression — 14 citations into it
- Safety, Robustness & Interpretability — 13 citations into it
- Aerospace Engineering — 10 citations into it
Whom Junsuk Choe trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Karen Simonyan
- Ross B. Girshick
- Christian Szegedy
- Andrew Zisserman
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
Who trusts Junsuk Choe overall
- Dingwen Zhang
- Sangdoo Yun
- Zeyi Huang
- Peng-Tao Jiang
- Mohammed Hassanin
- Zizhao Zhang
- Zhiqiang Shen
- Qibin Hou
- Junwei Han
- Zhengyuan Yang
Papers from Junsuk Choe's perspective
Ordered by how strongly the researchers Junsuk Choe trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- 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
- 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
- Attention-Based Dropout Layer for Weakly Supervised Object Localization (2019) — 412 citations