AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning

2018 ◽  
Author(s):  
Yogesh Malhotra
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Deep Learning ◽  
Generative Adversarial Networks ◽  
Model Risk ◽  
Adversarial Networks ◽  
Management Controls

scholarly journals Wasserstein Generative Adversarial Networks Based Data Augmentation for Radar Data Analysis

2020 ◽  
Vol 10 (4) ◽  
pp. 1449
Author(s):  
Hansoo Lee ◽  
Jonggeun Kim ◽  
Eun Kyeong Kim ◽  
Sungshin Kim
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Weather Radar ◽  
Image Synthesis ◽  
Radar Data ◽  
Generative Adversarial Networks ◽  
Necessary Condition ◽  
Radar Images ◽  
Adversarial Networks ◽  
Wide Range

Ground-based weather radar can observe a wide range with a high spatial and temporal resolution. They are beneficial to meteorological research and services by providing valuable information. Recent weather radar data related research has focused on applying machine learning and deep learning to solve complicated problems. It is a well-known fact that an adequate amount of data is a positively necessary condition in machine learning and deep learning. Generative adversarial networks (GANs) have received extensive attention for their remarkable data generation capacity, with a fascinating competitive structure having been proposed since. Consequently, a massive number of variants have been proposed; which model is adequate to solve the given problem is an inevitable concern. In this paper, we propose exploring the problem of radar image synthesis and evaluating different GANs with authentic radar observation results. The experimental results showed that the improved Wasserstein GAN is more capable of generating similar radar images while achieving higher structural similarity results.

Offline Handwritten Signature Authentication with Conditional Deep Convolutional Generative Adversarial Networks

2021 ◽  
Author(s):  
David C. Yonekura ◽  
Elloá B. Guedes
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Real World ◽  
Generative Adversarial Networks ◽  
Equal Error Rate ◽  
Training Time ◽  
Adversarial Networks ◽  
Handwritten Signature ◽  
State Of Art

Handwritten signature authentication systems are important in many real world scenarios to avoid frauds. Thanks to Deep Learning, state-of-art solutions have been proposed to this problem by making use of Convolutional Neural Networks, but other models in this Machine Learning subarea are still to be further explored. In this perspective, the present article introduces a Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) approach whose experimental results in a realistic dataset with skilled forgeries have Equal Error Rate (EER) of 18.53% and balanced accuracy of 87.91%. These results validate a writerdependent cDCGAN-based solution to the signature authentication problem in a real world scenario where no forgeries are available nor required in training time.

scholarly journals Image Colour Prediction using Deep Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 2037-2040
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Unsupervised Learning ◽  
Learning Task ◽  
Generative Adversarial Networks ◽  
Grayscale Image ◽  
Input File ◽  
Adversarial Networks

Any image we perceive through a screen is made of three separate channels, R, G, and B. With the help of these three channels; an image comes to colour. Any pictures taken during the old times were in grayscale format. To convert any given grayscale image into colour, we need the help of a photoshop professional, which might take hours of the workforce. In a revolution to this, we propose an utterly programmed methodology that produces lively and practical colourizations. Generative adversarial networks are an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input file to get an output. In our case, a grayscale image can be converted to colour with the help of GANs.

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.

scholarly journals Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4953
Author(s):  
Sara Al-Emadi ◽  
Abdulla Al-Ali ◽  
Abdulaziz Al-Ali
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Learning Algorithms ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Networks ◽  
Detection And Identification ◽  
Learning Techniques ◽  
The Impact

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

scholarly journals Independent attenuation correction of whole body [18F]FDG-PET using a deep learning approach with Generative Adversarial Networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Karim Armanious ◽  
Tobias Hepp ◽  
Thomas Küstner ◽  
Helmut Dittmann ◽  
Konstantin Nikolaou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Attenuation Correction ◽  
Fdg Pet ◽  
Whole Body ◽  
Generative Adversarial Networks ◽  
Learning Approach ◽  
Adversarial Networks ◽  
18F Fdg

Machine Learning Data Center Workloads Using Generative Adversarial Networks

2020 ◽  
Vol 48 (2) ◽  
pp. 21-23
Author(s):  
Boudewijn R. Haverkort ◽  
Felix Finkbeiner ◽  
Pieter-Tjerk de Boer
Keyword(s):  
Machine Learning ◽  
Data Center ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Learning Data
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