De-chuan Zhan

Artificial Intelligence · 2015–2024

A researcher in the Science corpus: 28 papers; trusts 2,590 researchers and is trusted by 47 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 1,238 citations into it; #4,479 of 35,345 by trust here, #4,393 by citations
• Computer Vision and Pattern Recognition — 988 citations into it; #4,460 of 21,575 by trust here, #4,088 by citations
• Meta & Continual Learning — 476 citations into it; #155 of 1,041 by trust here, #161 by citations
• Representation & Self-Supervised Learning — 427 citations into it; #1,033 of 3,145 by trust here, #1,068 by citations
• Optimization & Training — 158 citations into it
• Radiology, Nuclear Medicine and Imaging — 158 citations into it; #1,482 of 3,265 by trust here, #1,376 by citations
• Model Architectures — 91 citations into it
• Evaluation & Benchmarks — 81 citations into it

Whom De-chuan Zhan trusts overall

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

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

Who trusts De-chuan Zhan overall

1. Da-Wei Zhou
2. Han-Jia Ye
3. Qiwen Wang
4. Fu-Yun Wang
5. Chi Zhang
6. Wei-Lun Chao
7. Liyuan Wang
8. Hexiang Hu
9. Yaoyao Liu
10. Ji Zhang

Papers from De-chuan Zhan's perspective

Ordered by how strongly the researchers De-chuan Zhan 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. Gradient-based learning applied to document recognition
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

• Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions (2018) — 830 citations
• 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
• Identifying Ambiguous Similarity Conditions via Semantic Matching (2022) — 9 citations
• Tailoring Embedding Function to Heterogeneous Few-Shot Tasks by Global and Local Feature Adaptors (2021) — 29 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
• Contextualizing Multiple Tasks via Learning to Decompose (2021) — 5 citations
• Detecting Sequentially Novel Classes with Stable Generalization Ability (2021) — 10 citations
• Few-Shot Learning With a Strong Teacher (2021) — 82 citations
• Revisiting Meta-Learning as Supervised Learning (2020) — 24 citations