scholarly journals A GAN-Based Video Intra Coding

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 132
Author(s):  
Guangyu Zhong ◽  
Jun Wang ◽  
Jiyuan Hu ◽  
Fan Liang
Keyword(s):  
Video Coding ◽  
Prediction Accuracy ◽  
Intra Prediction ◽  
Linear Combinations ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Simulation Results ◽  
Spatial Redundancy ◽  
Prediction Approach ◽  
Fitting Ability

Intra prediction is a vital part of the image/video coding framework, which is designed to remove spatial redundancy within a picture. Based on a set of predefined linear combinations, traditional intra prediction cannot cope with coding blocks with irregular textures. To tackle this drawback, in this article, we propose a Generative Adversarial Network (GAN)-based intra prediction approach to enhance intra prediction accuracy. Specifically, with the superior non-linear fitting ability, the well-trained generator of GAN acts as a mapping from the adjacent reconstructed signals to the prediction unit, implemented into both encoder and decoder. Simulation results show that for All-Intra configuration, our proposed algorithm achieves, on average, a 1.6% BD-rate cutback for luminance components compared with video coding reference software HM-16.15 and outperforms previous similar works.

Generative Adversarial Network-Based Intra Prediction for Video Coding

2020 ◽  
Vol 22 (1) ◽  
pp. 45-58 ◽  
Author(s):  
Linwei Zhu ◽  
Sam Kwong ◽  
Yun Zhang ◽  
Shiqi Wang ◽  
Xu Wang
Keyword(s):  
Video Coding ◽  
Intra Prediction ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Remaining Useful Life Estimation Using Deep Convolutional Generative Adversarial Networks Based on an Autoencoder Scheme

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guisheng Hou ◽  
Shuo Xu ◽  
Nan Zhou ◽  
Lei Yang ◽  
Quanhao Fu
Keyword(s):  
Feature Extraction ◽  
Short Term Memory ◽  
Health Management ◽  
Remaining Useful Life ◽  
Fine Tuning ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Useful Life ◽  
Prediction Approach

Accurate predictions of remaining useful life (RUL) of important components play a crucial role in system reliability, which is the basis of prognostics and health management (PHM). This paper proposed an integrated deep learning approach for RUL prediction of a turbofan engine by integrating an autoencoder (AE) with a deep convolutional generative adversarial network (DCGAN). In the pretraining stage, the reconstructed data of the AE not only participate in its error reconstruction but also take part in the DCGAN parameter training as the generated data of the DCGAN. Through double-error reconstructions, the capability of feature extraction is enhanced, and high-level abstract information is obtained. In the fine-tuning stage, a long short-term memory (LSTM) network is used to extract the sequential information from the features to predict the RUL. The effectiveness of the proposed scheme is verified on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. The superiority of the proposed method is demonstrated via excellent prediction performance and comparisons with other existing state-of-the-art prognostics. The results of this study suggest that the proposed data-driven prognostic method offers a new and promising prediction approach and an efficient feature extraction scheme.

Enhancing the Prediction Accuracy of Solar Power Generation using a Generative Adversarial Network

2021 ◽  
Author(s):  
Kundjanasith Thonglek ◽  
Kohei Ichikawa ◽  
Keichi Takahashi ◽  
Chawanat Nakasan ◽  
Kazufumi Yuasa ◽  
...  
Keyword(s):  
Power Generation ◽  
Prediction Accuracy ◽  
Solar Power ◽  
Generative Adversarial Network ◽  
Solar Power Generation ◽  
Adversarial Network

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

Role of General Adversarial Networks in Mammogram Analysis: A Review

2020 ◽  
Vol 16 (7) ◽  
pp. 863-877
Author(s):  
Annapoorani Gopal ◽  
Lathaselvi Gandhimaruthian ◽  
Javid Ali
Keyword(s):  
Breast Tumor ◽  
Deep Neural Networks ◽  
Training Data ◽  
Learning Technology ◽  
Breast Cancers ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Tumor Extraction

The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.

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