Da-Wei Zhou

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 14 papers; trusts 1,552 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 765 citations into it; #5,643 of 35,345 by trust here, #6,229 by citations
• Computer Vision and Pattern Recognition — 616 citations into it; #4,990 of 21,575 by trust here, #5,754 by citations
• Meta & Continual Learning — 404 citations into it; #199 of 1,041 by trust here, #304 by citations
• Representation & Self-Supervised Learning — 183 citations into it; #2,461 of 3,145 by trust here, #2,217 by citations
• Radiology, Nuclear Medicine and Imaging — 95 citations into it; #1,956 of 3,265 by trust here, #1,825 by citations
• Optimization & Training — 82 citations into it
• Model Architectures — 53 citations into it
• Signal Processing — 46 citations into it

Whom Da-Wei Zhou trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ross B. Girshick
7. Yann LeCun
8. Oriol Vinyals
9. Ilya Sutskever
10. Jia Deng

Who trusts Da-Wei Zhou overall

1. Han-Jia Ye
2. Qiwen Wang
3. De-chuan Zhan
4. Fu-Yun Wang
5. Liyuan Wang
6. Keon-Hee Park
7. Dipam Goswami
8. Do-Yeon Kim
9. Zhiyuan Qi
10. Ziwei Liu

Papers from Da-Wei Zhou's perspective

Ordered by how strongly the researchers Da-Wei Zhou 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. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Forward Compatible Few-Shot Class-Incremental Learning (2022) — 288 citations
• Learning Placeholders for Open-Set Recognition (2021) — 267 citations
• Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks (2022) — 199 citations
• Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability (2019) — 112 citations
• Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning (2024) — 162 citations
• Deep Class-Incremental Learning: A Survey (2023) — 133 citations
• Class-Incremental Learning: A Survey (2023) — 380 citations
• Cost-Effective Incremental Deep Model: Matching Model Capacity With the Least Sampling (2023) — 57 citations
• A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning (2022) — 241 citations
• FOSTER: Feature Boosting and Compression for Class-Incremental Learning (2022) — 428 citations
• Co-Transport for Class-Incremental Learning (2021) — 100 citations
• Learning to Classify With Incremental New Class (2021) — 73 citations
• PyCIL: a Python toolbox for class-incremental learning (2021) — 145 citations
• Detecting Sequentially Novel Classes with Stable Generalization Ability (2021) — 10 citations