Gyeong-Moon Park

Computer Vision and Pattern Recognition · 2023–2024

A researcher in the Science corpus: 2 papers; trusts 290 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 53 citations into it; #19,804 of 21,575 by trust here, #20,756 by citations
• Artificial Intelligence — 51 citations into it; #32,229 of 35,345 by trust here, #33,294 by citations
• Meta & Continual Learning — 23 citations into it
• Generative Models — 18 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Radiology, Nuclear Medicine and Imaging — 6 citations into it
• Efficiency & Compression — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Gyeong-Moon Park trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Yoshua Bengio
5. Kaiming He
6. Yann LeCun
7. Ilya Sutskever
8. Tero Karras
9. Oriol Vinyals
10. Jimmy Ba

Who trusts Gyeong-Moon Park overall

1. Keon-Hee Park
2. Kyungwoo Song

Papers from Gyeong-Moon Park's perspective

Ordered by how strongly the researchers Gyeong-Moon Park 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. Learning Multiple Layers of Features from Tiny Images
8. ImageNet Large Scale Visual Recognition Challenge
9. Gradient-based learning applied to document recognition
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Pre-trained Vision and Language Transformers are Few-Shot Incremental Learners (2024) — 60 citations
• LFS-GAN: Lifelong Few-Shot Image Generation (2023) — 22 citations