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GENERATIVE ADVERSARIAL NETWORK AND DIGITAL ART INTERACTIONS WITH METAVERSE MARKETING

Yıl 2023, Cilt: 25 Sayı: 2, 375 - 396, 29.12.2023

Kemal Gökhan Nalbant Sevgi Aydın Şevval Uyanık

https://doi.org/10.26468/trakyasobed.1301771
Cited By: 2

Öz

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.

Anahtar Kelimeler

Generative Adversarial Network (GAN) Digital Art Artificial Intelligence Metaverse Marketing Digital Marketing

Kaynakça

  • Aggarwal, A., Mittal, M., & Battineni, G. (2021). Generative adversarial network: An overview of theory and applications. International Journal of Information Management Data Insights, 1(1), 100004. DOI:10.1016/j.jjimei.2020.100004
  • Anadol, R. (2018). Machine hallucinations: ISS dreams. Access Date: 28/03/2023. https://refikanadol.com/works/machinehallucinations-iss/
  • Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018). Generative adversarial networks: An overview. IEEE Signal Processing Magazine, 35(1), 53-65.
  • Dobilas, S. (2022). cGAN: Conditional Generative Adversarial Network — How to gain control over GAN outputs. Access Date: 02/11/2022. https://towardsdatascience.com/cgan-conditional-generative-adversarial-network-how-to-gain-control-over-gan-outputs-b30620bd0cc8
  • Droitcour, B. (2021). GANs and NFTs. Access Date: 08/04/2023. https://www.artnews.com/list/art-in-america/features/gans-and-nfts-1234594335/robbie-barrat-ai-generated-nude-portrait/

ÇEKİŞMELİ ÜRETİCİ AĞ VE METAVERSE PAZARLAMA İLE DİJİTAL SANAT ETKİLEŞİMLERİ

Yıl 2023, Cilt: 25 Sayı: 2, 375 - 396, 29.12.2023

Kemal Gökhan Nalbant Sevgi Aydın Şevval Uyanık

https://doi.org/10.26468/trakyasobed.1301771
Cited By: 2

Öz

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 GAN'ın temel yönlerini açıklamaya odaklanmaktadır. Eşzamanlı olarak, dijital sanatın tanımı, pratik uygulamaları, metaverse ve dijital pazarlama ile ilişkisi ile ilgili sorgulamalar irdelenmektedir.

Anahtar Kelimeler

Çekişmeli Üretici Ağ Dijital Sanat Yapay Zeka Metaverse Pazarlama Dijital Pazarlama

Kaynakça

  • Aggarwal, A., Mittal, M., & Battineni, G. (2021). Generative adversarial network: An overview of theory and applications. International Journal of Information Management Data Insights, 1(1), 100004. DOI:10.1016/j.jjimei.2020.100004
  • Anadol, R. (2018). Machine hallucinations: ISS dreams. Access Date: 28/03/2023. https://refikanadol.com/works/machinehallucinations-iss/
  • Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018). Generative adversarial networks: An overview. IEEE Signal Processing Magazine, 35(1), 53-65.
  • Dobilas, S. (2022). cGAN: Conditional Generative Adversarial Network — How to gain control over GAN outputs. Access Date: 02/11/2022. https://towardsdatascience.com/cgan-conditional-generative-adversarial-network-how-to-gain-control-over-gan-outputs-b30620bd0cc8
  • Droitcour, B. (2021). GANs and NFTs. Access Date: 08/04/2023. https://www.artnews.com/list/art-in-america/features/gans-and-nfts-1234594335/robbie-barrat-ai-generated-nude-portrait/
Toplam 5 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Pazarlama (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Kemal Gökhan Nalbant 0000-0002-5065-2504

Sevgi Aydın 0000-0002-9507-5448

Şevval Uyanık 0000-0002-0277-5833

Yayımlanma Tarihi 29 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 25 Sayı: 2

Kaynak Göster

APA Nalbant, K. G., Aydın, S., & Uyanık, Ş. (2023). GENERATIVE ADVERSARIAL NETWORK AND DIGITAL ART INTERACTIONS WITH METAVERSE MARKETING. Trakya Üniversitesi Sosyal Bilimler Dergisi, 25(2), 375-396. https://doi.org/10.26468/trakyasobed.1301771
AMA Nalbant KG, Aydın S, Uyanık Ş. GENERATIVE ADVERSARIAL NETWORK AND DIGITAL ART INTERACTIONS WITH METAVERSE MARKETING. Trakya University Journal of Social Science. Aralık 2023;25(2):375-396. doi:10.26468/trakyasobed.1301771
Chicago Nalbant, Kemal Gökhan, Sevgi Aydın, ve Şevval Uyanık. “GENERATIVE ADVERSARIAL NETWORK AND DIGITAL ART INTERACTIONS WITH METAVERSE MARKETING”. Trakya Üniversitesi Sosyal Bilimler Dergisi 25, sy. 2 (Aralık 2023): 375-96. https://doi.org/10.26468/trakyasobed.1301771.
EndNote Nalbant KG, Aydın S, Uyanık Ş (01 Aralık 2023) GENERATIVE ADVERSARIAL NETWORK AND DIGITAL ART INTERACTIONS WITH METAVERSE MARKETING. Trakya Üniversitesi Sosyal Bilimler Dergisi 25 2 375–396.
IEEE K. G. Nalbant, S. Aydın, ve Ş. Uyanık, “GENERATIVE ADVERSARIAL NETWORK AND DIGITAL ART INTERACTIONS WITH METAVERSE MARKETING”, Trakya University Journal of Social Science, c. 25, sy. 2, ss. 375–396, 2023, doi: 10.26468/trakyasobed.1301771.
ISNAD Nalbant, Kemal Gökhan vd. “GENERATIVE ADVERSARIAL NETWORK AND DIGITAL ART INTERACTIONS WITH METAVERSE MARKETING”. Trakya Üniversitesi Sosyal Bilimler Dergisi 25/2 (Aralık 2023), 375-396. https://doi.org/10.26468/trakyasobed.1301771.
JAMA Nalbant KG, Aydın S, Uyanık Ş. GENERATIVE ADVERSARIAL NETWORK AND DIGITAL ART INTERACTIONS WITH METAVERSE MARKETING. Trakya University Journal of Social Science. 2023;25:375–396.
MLA Nalbant, Kemal Gökhan vd. “GENERATIVE ADVERSARIAL NETWORK AND DIGITAL ART INTERACTIONS WITH METAVERSE MARKETING”. Trakya Üniversitesi Sosyal Bilimler Dergisi, c. 25, sy. 2, 2023, ss. 375-96, doi:10.26468/trakyasobed.1301771.
Vancouver Nalbant KG, Aydın S, Uyanık Ş. GENERATIVE ADVERSARIAL NETWORK AND DIGITAL ART INTERACTIONS WITH METAVERSE MARKETING. Trakya University Journal of Social Science. 2023;25(2):375-96.

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Resim

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