Hanbin Zhao

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 523 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 238 citations into it; #26,773 of 35,345 by trust here, #12,974 by citations
• Computer Vision and Pattern Recognition — 230 citations into it; #16,422 of 21,575 by trust here, #8,952 by citations
• Meta & Continual Learning — 107 citations into it
• Representation & Self-Supervised Learning — 68 citations into it
• Model Architectures — 33 citations into it
• Optimization & Training — 32 citations into it
• Radiology, Nuclear Medicine and Imaging — 29 citations into it
• Efficiency & Compression — 26 citations into it

Whom Hanbin Zhao trusts overall

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

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

Who trusts Hanbin Zhao overall

1. Da-Wei Zhou
2. Han-Jia Ye
3. Qiwen Wang
4. Jinpeng Chen
5. Huiping Zhuang
6. Man Zhou
7. Songlin Dong
8. De-chuan Zhan
9. Mengya Gao
10. Yiduo Guo

Papers from Hanbin Zhao's perspective

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

Their papers

• When Video Classification Meets Incremental Classes (2021) — 32 citations
• Memory-Efficient Class-Incremental Learning for Image Classification (2020) — 108 citations
• Progressive Blockwise Knowledge Distillation for Neural Network Acceleration (2018) — 73 citations
• What and Where: Learn to Plug Adapters via NAS for Multidomain Learning (2020) — 21 citations
• MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental Learning (2020) — 151 citations
• Few-Shot Class-Incremental Learning via Feature Space Composition (2020) — 6 citations