Shunquan Tan

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 54 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 34 citations into it
• Generative Models — 18 citations into it
• Artificial Intelligence — 13 citations into it
• Model Architectures — 7 citations into it
• Optimization & Training — 5 citations into it

Whom Shunquan Tan trusts overall

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

1. Jessica J. Fridrich
2. Sergey Ioffe
3. Christian Szegedy
4. Tomáš Filler
5. Alex Krizhevsky
6. Geoffrey E. Hinton
7. Karen Simonyan
8. Individualized Treat
9. Kaiming He
10. Yann LeCun

Who trusts Shunquan Tan overall

1. Matthew Tancik

Papers from Shunquan Tan's perspective

Ordered by how strongly the researchers Shunquan Tan 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. Gradient-based learning applied to document recognition
4. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
5. Going deeper with convolutions
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. GENERATIVE ADVERSARIAL NETS
9. Attention is All you Need (Neural Information Processing Systems)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Can Forensic Detectors Identify GAN Generated Images? (2018) — 23 citations
• Automatic Steganographic Distortion Learning Using a Generative Adversarial Network (2017) — 361 citations