Seung-Jun Han

Generative Models · 2020

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Generative Models — 24 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Artificial Intelligence — 12 citations into it

Whom Seung-Jun Han trusts overall

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

1. Sebastian Nowozin
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Jacob Menick
5. Karen Simonyan
6. Nal Kalchbrenner
7. Andrew Brock
8. Olaf Ronneberger
9. Alex Krizhevsky
10. Yoshua Bengio

Papers from Seung-Jun Han's perspective

Ordered by how strongly the researchers Seung-Jun Han 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget (2020) — 10 citations