Sanghyuk Chun

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 7 papers; trusts 955 researchers and is trusted by 454 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 240 citations into it; #2,002 of 21,575 by trust here, #1,776 by citations
• Artificial Intelligence — 218 citations into it; #6,235 of 35,345 by trust here, #2,700 by citations
• Representation & Self-Supervised Learning — 44 citations into it
• Optimization & Training — 43 citations into it; #907 of 2,601 by trust here, #275 by citations
• Model Architectures — 42 citations into it
• Evaluation & Benchmarks — 41 citations into it
• Safety, Robustness & Interpretability — 25 citations into it
• Multimodal Learning — 15 citations into it

Whom Sanghyuk Chun trusts overall

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

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

Who trusts Sanghyuk Chun overall

1. Zhiqiang Shen
2. Sachin Mehta
3. Chun-Fu Chen
4. Weihao Yu
5. Ali Hassani
6. Kaiyang Zhou
7. Kai Han
8. Hugo Touvron
9. Li Yuan
10. Taesung Park

Papers from Sanghyuk Chun's perspective

Ordered by how strongly the researchers Sanghyuk Chun 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features (2019) — 6,079 citations
• Photorealistic Style Transfer via Wavelet Transforms (2019) — 425 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
• SWAD: Domain Generalization by Seeking Flat Minima (2021) — 606 citations
• Probabilistic Embeddings for Cross-Modal Retrieval (2021) — 306 citations
• Learning De-biased Representations with Biased Representations (2019) — 339 citations