Search Results for author: Jinglan Liu

Found 6 papers, 1 papers with code

Multi-Cycle-Consistent Adversarial Networks for CT Image Denoising

no code implementations27 Feb 2020 Jinglan Liu, Yukun Ding, JinJun Xiong, Qianjun Jia, Meiping Huang, Jian Zhuang, Bike Xie, Chun-Chen Liu, Yiyu Shi

For example, if the noise is large leading to significant difference between domain $X$ and domain $Y$, can we bridge $X$ and $Y$ with an intermediate domain $Z$ such that both the denoising process between $X$ and $Z$ and that between $Z$ and $Y$ are easier to learn?

Image Denoising Image-to-Image Translation +1

Uncertainty-Aware Training of Neural Networks for Selective Medical Image Segmentation

no code implementations MIDL 2019 Yukun Ding, Jinglan Liu, Xiaowei Xu, Meiping Huang, Jian Zhuang, JinJun Xiong, Yiyu Shi

Existing selective segmentation methods, however, ignore this unique property of selective segmentation and train their DNN models by optimizing accuracy on the entire dataset.

Image Segmentation Medical Image Segmentation +2

Revisiting the Evaluation of Uncertainty Estimation and Its Application to Explore Model Complexity-Uncertainty Trade-Off

1 code implementation5 Mar 2019 Yukun Ding, Jinglan Liu, JinJun Xiong, Yiyu Shi

Accurately estimating uncertainties in neural network predictions is of great importance in building trusted DNNs-based models, and there is an increasing interest in providing accurate uncertainty estimation on many tasks, such as security cameras and autonomous driving vehicles.

Autonomous Driving

PBGen: Partial Binarization of Deconvolution-Based Generators for Edge Intelligence

no code implementations26 Feb 2018 Jinglan Liu, Jiaxin Zhang, Yukun Ding, Xiaowei Xu, Meng Jiang, Yiyu Shi

This work explores the binarization of the deconvolution-based generator in a GAN for memory saving and speedup of image construction.


On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks

no code implementations ICLR 2019 Yukun Ding, Jinglan Liu, JinJun Xiong, Yiyu Shi

To the best of our knowledge, this is the first in-depth study on the complexity bounds of quantized neural networks.


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