Hau-San Wong

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 181 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 59 citations into it
• Artificial Intelligence — 57 citations into it
• Representation & Self-Supervised Learning — 41 citations into it
• Generative Models — 16 citations into it

Whom Hau-San Wong trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Mingsheng Long
8. Ilya Sutskever
9. Jimmy Ba
10. Ross B. Girshick

Papers from Hau-San Wong's perspective

Ordered by how strongly the researchers Hau-San Wong 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Model Adaptation: Unsupervised Domain Adaptation Without Source Data (2020) — 585 citations
• Improving Domain-Specific Classification by Collaborative Learning with Adaptation Networks (2019) — 13 citations