Minyoung Huh

Computer Vision and Pattern Recognition · 2016–2024

A researcher in the Science corpus: 5 papers; trusts 1,197 researchers and is trusted by 423 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 224 citations into it; #2,039 of 21,575 by trust here, #4,245 by citations
• Artificial Intelligence — 116 citations into it; #6,934 of 35,345 by trust here, #6,848 by citations
• Generative Models — 82 citations into it; #1,687 of 1,956 by trust here, #1,382 by citations
• Representation & Self-Supervised Learning — 48 citations into it
• Evaluation & Benchmarks — 22 citations into it; #937 of 3,147 by trust here, #1,212 by citations
• Media Technology — 22 citations into it
• Computer Graphics and Computer-Aided Design — 19 citations into it
• Large Language Models — 16 citations into it

Whom Minyoung Huh trusts overall

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

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

Who trusts Minyoung Huh overall

1. Yu-Xiong Wang
2. Davide Cozzolino
3. Edouard Oyallon
4. Jun-Yan Zhu
5. Daiqing Li
6. Deva Ramanan
7. Chen Sun
8. Xiaohua Zhai
9. Alexei A. Efros
10. Congzheng Song

Papers from Minyoung Huh's perspective

Ordered by how strongly the researchers Minyoung Huh 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
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

• What makes ImageNet good for transfer learning? (2016) — 714 citations
• Transforming and Projecting Images into Class-conditional Generative Networks (2020) — 116 citations
• Fighting Fake News: Image Splice Detection via Learned Self-Consistency (2018) — 473 citations
• Feedback Adversarial Learning: Spatial Feedback for Improving Generative Adversarial Networks (2019) — 26 citations
• The Platonic Representation Hypothesis (2024) — 392 citations