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Oct 1, 2018 · Yu-Ming is one of only a few Australian radiologists with first-author publications in both the American Journal of Roentgenology (AJR) and American Journal of Neuroradiology (AJNR). He also has publications in the Australasian Radiology, Skeletal Radiology and Spinal Cord.
Apr 19, 2022 · Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data. Yu-Ming Tang, Yi-Xing Peng, Wei-Shi Zheng. Deep neural network (DNN) suffers from catastrophic forgetting when learning incrementally, which greatly limits its applications.
- arXiv:2204.08932 [cs.CV]
- Accepted to CVPR2022
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Aug 21, 2023 · Yu-Ming Tang, Yi-Xing Peng, Wei-Shi Zheng. Incremental learning aims to overcome catastrophic forgetting when learning deep networks from sequential tasks. With impressive learning efficiency and performance, prompt-based methods adopt a fixed backbone to sequential tasks by learning task-specific prompts. However, existing prompt-based methods ...
ORCID record for Yu-Ming Tang. ORCID provides an identifier for individuals to use with their name as they engage in research, scholarship, and innovation activities.
Yu-Ming Tang, Yi-Xing Peng, Wei-Shi Zheng, "Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data", Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2022.
Yu-Ming Tang, Yi-Xing Peng, Wei-Shi Zheng: Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data. CVPR 2022: 9539-9548
Yu-Ming Tang, Yi-Xing Peng, Wei-Shi Zheng; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 1706-1716. Abstract. Incremental learning aims to overcome catastrophic forgetting when learning deep networks from sequential tasks.
