Sculpture Generation Using Generative Adversarial Network : Exploring GAN

Jainulabudeen Sheik Abdul Kareem

ISBN 10: 6200216584 / ISBN 13: 9786200216588

Verlag: LAP LAMBERT Academic Publishing, 2019

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Bestandsnummer des Verkäufers 9786200216588

Bibliografische Details

• Titel: Sculpture Generation Using Generative Adversarial Network : Exploring GAN
• Verlag: LAP LAMBERT Academic Publishing
• Erscheinungsjahr: 2019
• Sprache: Englisch
• ISBN-10: 6200216584
• ISBN-13: 9786200216588
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 220B x 150 H x 4 T Millimeter Breite: 220 mm, Höhe: 150 mm, Tiefe: 4 mm
• Gewicht: 107 Gramm

Über diesen Titel

Inhaltsangabe

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.