Keqiuyin Li bio photo

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Journal Paper

  • Federated Fuzzy Transfer Learning with Domain and Category Shifts, IEEE Transactions on Fuzzy System, 2024. Paper Code
  • Multi-Source Domain Adaptation Handling Inaccurate Label Spaces, Neurocomputing, 2024. Paper
  • Source-Free Unsupervised Domain Adaptation: Current Research and Future Directions, Neurocomputing, 2023. Paper
  • Source-free multi-domain adaptation with fuzzy rule-based deep neural networks, IEEE Transactions on Fuzzy System, 2023. Paper Code
  • Multidomain adaptation with sample and source distillation, IEEE Transactions on Cybernetics, 2023. Paper Code
  • Dynamic classifier alignment for unsupervised multi-source domain adaptation, IEEE Transactions on Knowledge and Data Engineering, 2022. Paper Code
  • Multi-source contribution learning for domain adaptation, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 4, 2021. Paper Code
  • Super-resolution using neighbourhood regression with local structure prior, Signal Processing: Image Communication, vol 72, 2019, pp: 58-68. Paper Code
  • A new method for image super-resolution with multi-channel constraints, Knowledge-Based Systems, vol 146, 2018, pp:118-128. Paper Code

Conference Paper

  • Domain Adaptation for Image Segmentation with Category-Guide Classifier, in Proceedings of International Conference on Intelligent Systems and Knowledge Engineering (ISKE) 2023. (Best Paper Award)
  • Multi-Source Domain Adaptation with Incomplete Source Label Spaces, in Proceedings of International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES) 2023.
  • Attention-Bridging TS Fuzzy Rules for Universal Multi-Domain Adaptation without Source Data, in Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2023.(Best Paper Award) Paper
  • Source-free multi-domain adaptation with generally auxiliary model training, in Proceedings of the International Joint Conference on Neural Networks (IJCNN), Italy, July 18-23, 2022. Paper Code
  • Multi-source domain adaptation with fuzzy-rule based deep neural networks, in Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Virtual Online: IEEE, July 11 - 14 2021, pp. 1–6. (Best Paper Finalists) Paper
  • Multi-source domain adaptation with distribution fusion and relationship extraction, in Proceedings of the International Joint Conference on Neural Networks (IJCNN). Virtual online: IEEE, July 19 - 24 2020, pp. 1–6. Paper

Thesis

  • Deep Neural Networks for Multi-Source Transfer Learning, Doctoral dissertation, University of Technology Sydney, 2022. Paper