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Duo Wang
Researcher at Tsinghua University
Publications - 11
Citations - 1438
Duo Wang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Deep learning & Image segmentation. The author has an hindex of 5, co-authored 10 publications receiving 967 citations. Previous affiliations of Duo Wang include Brigham and Women's Hospital.
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A Survey of Model Compression and Acceleration for Deep Neural Networks
TL;DR: This paper survey the recent advanced techniques for compacting and accelerating CNNs model developed, roughly categorized into four schemes: parameter pruning and sharing, low-rank factorization, transferred/compact convolutional filters, and knowledge distillation.
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Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges
TL;DR: It is shown that the top face-verification results from the Labeled Faces in the Wild data set were obtained with networks containing hundreds of millions of parameters, using a mix of convolutional, locally connected, and fully connected layers.
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A hybrid approach with optimization-based and metric-based meta-learner for few-shot learning
TL;DR: This work proposes a hybrid meta-learning model called Meta-Metric-Learner which combines the merits of both optimization- and metric-based approaches and is able to handle flexible numbers of classes as well as generate more generalized metrics for classification across tasks.
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Mixed-Supervised Dual-Network for Medical Image Segmentation.
TL;DR: This paper proposes Mixed-Supervised Dual-Network (MSDN), a novel architecture which consists of two separate networks for the detection and segmentation tasks respectively, and a series of connection modules between the layers of the two networks.
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3D deep learning based classification of pulmonary ground glass opacity nodules with automatic segmentation.
Duo Wang,Duo Wang,Tao Zhang,Ming Li,Raphael Bueno,Raphael Bueno,Jagadeesan Jayender,Jagadeesan Jayender +7 more
TL;DR: A joint deep learning model where the segmentation can better facilitate the classification of pulmonary GGNs is proposed, and experimental results show that the proposed method outperforms other baseline models on all the diagnostic classification tasks.