Search Results for author: Shen Zheng

Found 8 papers, 4 papers with code

AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAE

no code implementations28 Jun 2022 Changjie Lu, Shen Zheng, ZiRui Wang, Omar Dib, Gaurav Gupta

However, due to the unavailability of an effective metric to evaluate the difference between the real and the fake images, the posterior collapse and the vanishing gradient problem still exist, reducing the fidelity of the synthesized images.

Image Generation

Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

1 code implementation20 Apr 2022 Changjie Lu, Shen Zheng, Gaurav Gupta

This paper introduces UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation with a compact loss function lower bound.

Cardiac Segmentation Image Segmentation +3

Asian Giant Hornet Control based on Image Processing and Biological Dispersal

no code implementations26 Nov 2021 Changjie Lu, Shen Zheng, Hailu Qiu

Fourth, we apply ordinary differential equations to examine AGH numbers at the different natural growthrate and reaction speed and output the potential propagation coefficient.

Feature Importance

SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Deraining

1 code implementation17 Nov 2021 Shen Zheng, Changjie Lu, Yuxiong Wu, Gaurav Gupta

To address this issue, in this paper, we present a segmentation-aware progressive network (SAPNet) based upon contrastive learning for single image deraining.

Contrastive Learning Image Restoration +4

Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement

1 code implementation3 Oct 2021 Shen Zheng, Gaurav Gupta

Firstly, we design an enhancement factor extraction network using depthwise separable convolution for an efficient estimate of the pixel-wise light deficiency of an low-light image.

Low-Light Image Enhancement Unsupervised Semantic Segmentation +2

Exploiting Spline Models for the Training of Fully Connected Layers in Neural Network

no code implementations12 Feb 2021 Kanya Mo, Shen Zheng, Xiwei Wang, Jinghua Wang, Klaus-Dieter Schewe

The fully connected (FC) layer, one of the most fundamental modules in artificial neural networks (ANN), is often considered difficult and inefficient to train due to issues including the risk of overfitting caused by its large amount of parameters.

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