World's Best Scientists 2026 revealed!
De-Chuan Zhan

De-Chuan Zhan

D-Index & Metrics

Computer Science

D-Index
32
Citations
4368
World Ranking
13175
National Ranking
1609

Best Publications

  • Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions

    Han-Jia Ye;Hexiang Hu;De-Chuan Zhan;Fei Sha

  • FOSTER: Feature Boosting and Compression for Class-Incremental Learning

    Unknown

  • Forward Compatible Few-Shot Class-Incremental Learning

    Unknown

  • Learning Placeholders for Open-Set Recognition

    Unknown

  • Semi-supervised learning with very few labeled training examples

    Zhi-Hua Zhou;De-Chuan Zhan;Qiang Yang

  • Class-Incremental Learning: A Survey

    Unknown

  • Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks

    Unknown

  • FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data

    Unknown

  • Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need

    Unknown

  • FedPHP: Federated Personalization with Inherited Private Models.

    Unknown

  • Multi-modal image annotation with multi-instance multi-label LDA

    Cam-Tu Nguyen;De-Chuan Zhan;Zhi-Hua Zhou

  • Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability

    Yang Yang;Da-Wei Zhou;De-Chuan Zhan;Hui Xiong

  • Learning Embedding Adaptation for Few-Shot Learning

    Han-Jia Ye;Hexiang Hu;De-Chuan Zhan;Fei Sha

  • Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions

    Han-Jia Ye;Hexiang Hu;De-Chuan Zhan;Fei Sha

  • Co-Transport for Class-Incremental Learning

    Unknown

  • PyCIL: a Python toolbox for class-incremental learning

    Unknown

  • Learning instance specific distances using metric propagation

    De-Chuan Zhan;Ming Li;Yu-Feng Li;Zhi-Hua Zhou

  • Class-Incremental Learning: A Survey

    Unknown

  • Few-Shot Learning with a Strong Teacher.

    Unknown

  • Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning

    Unknown

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