Sculpture Generation Using Generative Adversarial Network

Jainulabudeen Sheik Abdul Kareem

ISBN 10: 6200216584 / ISBN 13: 9786200216588

Published by LAP LAMBERT Academic Publishing Jun 2019, 2019

About this Item

Neuware -The purpose of this book chapter is to provide a approach to explore the Machine's Capability in generating human-level products as well as problem solving. Sculptures became a part of finest medium in South Indian Expression in form of dance. This book chapter entails the use of GAN (Generative Adversarial Network) in Deep Neural Net Architectures called the Generator (New Data Instances) and Discriminator (Authenticity). GAN has the following phases. The First Phase Generator takes input in random numbers and return an image. In the Second Phase the Generated Image is fed into the Discriminator. In the Final Phase the Discriminator takes both real and fake images and return probabilities for representing Authenticity. The reader is expected to be familiar with the working process and basic features of GAN.Books on Demand GmbH, Überseering 33, 22297 Hamburg 60 pp. Englisch.

Seller Inventory # 9786200216588

Bibliographic Details

• Title: Sculpture Generation Using Generative Adversarial Network
• Publisher: LAP LAMBERT Academic Publishing Jun 2019
• Publication Date: 2019
• Language: English
• ISBN 10: 6200216584
• ISBN 13: 9786200216588
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 107 grams

About this title

Synopsis

The purpose of this book chapter is to provide a approach to explore the Machine's Capability in generating human-level products as well as problem solving. Sculptures became a part of finest medium in South Indian Expression in form of dance. This book chapter entails the use of GAN (Generative Adversarial Network) in Deep Neural Net Architectures called the Generator (New Data Instances) and Discriminator (Authenticity). GAN has the following phases. The First Phase Generator takes input in random numbers and return an image. In the Second Phase the Generated Image is fed into the Discriminator. In the Final Phase the Discriminator takes both real and fake images and return probabilities for representing Authenticity. The reader is expected to be familiar with the working process and basic features of GAN.