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Jia-Hong Lee

Researcher at Academia Sinica

Publications -  16
Citations -  816

Jia-Hong Lee is an academic researcher from Academia Sinica. The author has contributed to research in topics: Deep learning & Artificial neural network. The author has an hindex of 6, co-authored 16 publications receiving 535 citations. Previous affiliations of Jia-Hong Lee include National Taiwan University of Science and Technology & National Taiwan University.

Papers
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Journal ArticleDOI

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

TL;DR: This paper proposes a common evaluation framework for automatic stroke lesion segmentation from MRIP, describes the publicly available datasets, and presents the results of the two sub‐challenges: Sub‐Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES).
Journal ArticleDOI

A benchmark for comparison of dental radiography analysis algorithms

TL;DR: Based on the quantitative evaluation results, it is believed automatic dental radiography analysis is still a challenging and unsolved problem and the datasets and the evaluation software are made available to the research community, further encouraging future developments in this field.
Proceedings ArticleDOI

Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning

TL;DR: The proposed packing-and-expanding method is effective and easy to implement, which can iteratively shrink and enlarge the model to integrate new functions and maintains the compactness in continual learning.
Proceedings ArticleDOI

Joint Estimation of Age and Gender from Unconstrained Face Images Using Lightweight Multi-Task CNN for Mobile Applications

TL;DR: Lightweight multi-task CNN as discussed by the authors uses depthwise separable convolution to reduce the model size and save the inference time for age and gender classification on the public challenging Adience dataset.
Proceedings ArticleDOI

Unifying and Merging Well-trained Deep Neural Networks for Inference Stage

TL;DR: The authors aligns the layers of the original networks and merges them into a unified model by sharing the representative codes of weights and further re-train the shared weights to fine-tune the performance of the merged model.