GENERATIVE ADVERSARIAL NETWORK AND DIGITAL ART INTERACTIONS WITH METAVERSE MARKETING

Kemal Gökhan Nalbant; Sevgi Aydın; Şevval Uyanık

Makale detayları

GENERATIVE ADVERSARIAL NETWORK AND DIGITAL ART INTERACTIONS WITH METAVERSE MARKETING

Yazarlar:

Kemal Gökhan Nalbant | Sevgi Aydın | Şevval Uyanık

Yayıncı:

Trakya University

Ana Başlıklar:

Pazarlama (Diğer)

Dergi:

Trakya Üniversitesi Sosyal Bilimler Dergisi Vol.25 Issue 2, pp.375-396

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001306538

pISSN:

1305-7766

Anahtar kelimeler:

Özet:

 The application of machine learning, deep learning, and artificial intelligence is ubiquitous across various domains. The Generative Adversarial Network GAN is considered a remarkable deep learning architecture among its peers. Provided that an ample quantity of data samples is fed to the GAN model, it is feasible to generate novel samples of the same data category. By providing the system with a large dataset of cat images, it can acquire the ability to recognize the defining characteristics of a feline and subsequently produce novel cat photos. This architectural design served as the foundation for numerous programs. The domain of digital art has experienced significant impact in recent times. The GAN has emerged as a prominent deep learning framework that has had a significant impact on the field of digital art. This article primarily focuses on elucidating the fundamental aspects of GAN, including its definition, operational mechanism, classification, practical implementations, and correlation with digital art. Simultaneously, inquiries pertaining to the definition of digital art, its practical implementations, and its correlation with the metaverse and digital marketing are being scrutinized.

 The application of machine learning, deep learning, and artificial intelligence is ubiquitous across various domains. The Generative Adversarial Network GAN is considered a remarkable deep learning architecture among its peers. Provided that an ample quantity of data samples is fed to the GAN model, it is feasible to generate novel samples of the same data category. By providing the system with a large dataset of cat images, it can acquire the ability to recognize the defining characteristics of a feline and subsequently produce novel cat photos. This architectural design served as the foundation for numerous programs. The domain of digital art has experienced significant impact in recent times. The GAN has emerged as a prominent deep learning framework that has had a significant impact on the field of digital art. This article primarily focuses on elucidating the fundamental aspects of GAN, including its definition, operational mechanism, classification, practical implementations, and correlation with digital art. Simultaneously, inquiries pertaining to the definition of digital art, its practical implementations, and its correlation with the metaverse and digital marketing are being scrutinized.

 The application of machine learning, deep learning, and artificial intelligence is ubiquitous across various domains. The Generative Adversarial Network GAN is considered a remarkable deep learning architecture among its peers. Provided that an ample quantity of data samples is fed to the GAN model, it is feasible to generate novel samples of the same data category. By providing the system with a large dataset of cat images, it can acquire the ability to recognize the defining characteristics of a feline and subsequently produce novel cat photos. This architectural design served as the foundation for numerous programs. The domain of digital art has experienced significant impact in recent times. The GAN has emerged as a prominent deep learning framework that has had a significant impact on the field of digital art. This article primarily focuses on elucidating the fundamental aspects of GAN, including its definition, operational mechanism, classification, practical implementations, and correlation with digital art. Simultaneously, inquiries pertaining to the definition of digital art, its practical implementations, and its correlation with the metaverse and digital marketing are being scrutinized.

 Makine öğrenimi, derin öğrenme ve yapay zeka uygulamaları çeşitli alanlarda her yerde bulunur. Çekişmeli Üretici Ağ GAN, emsalleri arasında dikkate değer bir derin öğrenme mimarisi olarak kabul edilir. GAN modeline bol miktarda veri örneğinin beslenmesi koşuluyla, aynı veri kategorisinden yeni örnekler oluşturmak mümkündür. Sisteme kedi görüntülerinden oluşan geniş bir veri kümesi sağlayarak, bir kedinin tanımlayıcı özelliklerini tanıma ve ardından yeni kedi fotoğrafları üretme becerisi kazanabilir. Bu mimari tasarım, çok sayıda programın temelini oluşturdu. Dijital sanatın alanı son zamanlarda önemli bir etki yaşadı. GAN, dijital sanat alanında önemli bir etkiye sahip olan, önde gelen bir derin öğrenme çerçevesi olarak ortaya çıkmıştır. Bu makale öncelikle tanımı, çalışma mekanizması, sınıflandırması, pratik uygulamaları ve dijital sanatla ilişkisi dahil olmak üzere GANın temel yönlerini açıklamaya odaklanmaktadır. Eşzamanlı olarak, dijital sanatın tanımı, pratik uygulamaları, metaverse ve dijital pazarlama ile ilişkisi ile ilgili sorgulamalar irdelenmektedir.

Makale detayları

GENERATIVE ADVERSARIAL NETWORK AND DIGITAL ART INTERACTIONS WITH METAVERSE MARKETING

Yazarlar:

Kemal Gökhan Nalbant | Sevgi Aydın | Şevval Uyanık

Yayıncı:

Trakya University

Ana Başlıklar:

Pazarlama (Diğer)

Dergi:

Trakya Üniversitesi Sosyal Bilimler Dergisi Vol.25 Issue 2, pp.375-396

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001306538

pISSN:

1305-7766

Generative Adversarial Network (GAN)

Digital Art

Artificial Intelligence

Metaverse Marketing

Digital MarketingÇekişmeli Üretici Ağ

Dijital Sanat

Yapay Zeka

Metaverse Pazarlama

Dijital Pazarlama

Sayı

Makale detayları

Yazarlar: Kemal Gökhan Nalbant | Sevgi Aydın | Şevval Uyanık

Yayıncı: Trakya University

Ana Başlıklar: Pazarlama (Diğer)

Dergi: Trakya Üniversitesi Sosyal Bilimler Dergisi Vol.25 Issue 2, pp.375-396

Belge Tipi: Makale

Yayın Tarihi: 12/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001306538

pISSN: 1305-7766

Sayı

Yazarlar:

Kemal Gökhan Nalbant | Sevgi Aydın | Şevval Uyanık

Yayıncı:

Trakya University

Ana Başlıklar:

Pazarlama (Diğer)

Dergi:

Trakya Üniversitesi Sosyal Bilimler Dergisi Vol.25 Issue 2, pp.375-396

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001306538

pISSN:

1305-7766