Kai Han

Computer Vision and Pattern Recognition · with Vedaldi · 2019–2022

A researcher in the Science corpus: 11 papers; trusts 1,193 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 300 citations into it; #5,339 of 21,575 by trust here, #7,249 by citations
• Artificial Intelligence — 264 citations into it; #6,859 of 35,345 by trust here, #10,430 by citations
• Representation & Self-Supervised Learning — 127 citations into it
• Optimization & Training — 40 citations into it
• Meta & Continual Learning — 37 citations into it
• Model Architectures — 33 citations into it
• Evaluation & Benchmarks — 29 citations into it
• Radiology, Nuclear Medicine and Imaging — 28 citations into it; #465 of 3,265 by trust here, #1,355 by citations

Whom Kai Han trusts overall

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

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

Who trusts Kai Han overall

1. K. J. Joseph
2. Bingchen Zhao
3. Sagar Vaze
4. Zhun Zhong
5. Xu Jia
6. Subhankar Roy
7. Sujoy Paul
8. Enrico Fini
9. Mohammadreza Iman
10. Gaurav Aggarwal

Papers from Kai Han's perspective

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

Their papers

• Automatically Discovering and Learning New Visual Categories with Ranking Statistics (2020) — 227 citations
• Learning to Discover Novel Visual Categories via Deep Transfer Clustering (2019) — 392 citations
• Unsupervised Image Matching and Object Discovery as Optimization (2019) — 69 citations
• Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation (2021) — 153 citations
• Joint Representation Learning and Novel Category Discovery on Single- and Multi-modal Data (2021) — 80 citations
• Open-Set Recognition: A Good Closed-Set Classifier is All You Need (2021) — 625 citations
• Generalized Category Discovery (2022) — 325 citations
• Spacing Loss for Discovering Novel Categories (2022) — 18 citations
• Novel Class Discovery without Forgetting (2022) — 61 citations
• Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification (2021) — 298 citations
• Anisotropic Convolutional Networks for 3D Semantic Scene Completion (2020) — 187 citations