Intelligent corner synthesis via cycle-consistent generative adversarial networks for efficient validation of autonomous driving systems

2018 ◽  
Author(s):  
Handi Yu ◽  
Xin Li
Keyword(s):  
Autonomous Driving ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

scholarly journals A comparison of deep learning algorithms on image data for detecting floodwater on roadways

2021 ◽  
pp. 58-58
Author(s):  
Salih Sarp ◽  
Murat Kuzlu ◽  
Yanxiao Zhao ◽  
Mecit Cetin ◽  
Ozgur Guler
Keyword(s):  
Object Detection ◽  
Traffic Management ◽  
Image Data ◽  
Autonomous Driving ◽  
Management Systems ◽  
Generative Adversarial Networks ◽  
Segmentation Method ◽  
Adversarial Networks ◽  
Segmentation Algorithms ◽  
Natural Settings

Object detection and segmentation algorithms evolved significantly in the last decade. Simultaneous object detection and segmentation paved the way for real-time applications such as autonomous driving. Detection and segmentation of (partially) flooded roadways are essential inputs for vehicle routing and traffic management systems. This paper proposes an automatic floodwater detection and segmentation method utilizing the Mask Region-Based Convolutional Neural Networks (Mask-R-CNN) and Generative Adversarial Networks (GAN) algorithms. To train the model, manually labeled images with urban, suburban, and natural settings are used. The performances of the algorithms are assessed in accurately detecting the floodwater captured in images. The results show that the proposed Mask-R-CNN-based floodwater detection and segmentation outperform previous studies, whereas the GAN-based model has a straightforward implementation compared to other models.

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.

A Primer on Generative Adversarial Networks

2020 ◽  
Author(s):  
Dr. Vikas Thada ◽  
Mr. Utpal Shrivastava ◽  
Jyotsna Sharma ◽  
Kuwar Prateek Singh ◽  
Manda Ranadeep
Keyword(s):  
Generative Adversarial Networks ◽  
Adversarial Networks
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