Derin Evrişim Tabanlı Çekişmeli Üretici Ağları İle Uçtan Uca Sanat Eserleri Üretimi

Year 2023, , 671 - 676, 28.06.2023

Nazlı Turhan , Ahmet Haşim Yurttakal

https://doi.org/10.35414/akufemubid.1269356

Abstract

Yapay zeka (AI) teknolojileri sağlık, eğitim, sanat gibi birçok alanda kullanılıp hızla gelişmeye devam ederken ortaya çıkan yapay zeka çözümleri, bilişim hukuku gibi farklı disiplinler tarafından da ele alınmaktadır. Hukuk kurallarının sosyal değişimin hızına erişim sorunları bir yana, değişime ayak uydurmaya müsait bir hukuki alt yapının varlığının araştırılması da son yıllarda önemini hissettirmeye başlamıştır. Çalışmada derin öğrenme algoritmalarından çekişmeli üretici ağlar kullanılarak oluşturulan dijital sanat eserlerinin teknik aşamaları ele alınarak fikir ve sanat eserleri hukuku kapsamında değerlendirilmiştir. Çalışmada Wiki-Art veri kümesinin bir alt kümesi olan 6989 adet soyut ve portre tablolar kullanılmıştır. Sonuç olarak veri kümesindeki görüntü sayısının çıktıların orijinalliğine etki ettiği görülmüştür. Önerilen yöntemin farklı sanat dallarına uygulanabileceği ve sanatseverlere farklı bir bakış açısı kazandırabileceği düşünülmektedir.

Keywords

Sanat Eserleri, Çekişmeli Üretici Ağlar, Uçtan uca, Yapay Zeka

End-to-End Artworks Generation Via Deep Convolutional Based Generative Adversarial Networks

Abstract

While artificial intelligence (AI) technologies are used in many fields such as health, education, art and continue to develop rapidly, emerging artificial intelligence solutions are also being addressed by different disciplines, such as informatics and law. Apart from the problems of legal rules' having access to the speed of social change, the search of a legal infrastructure that is suitable for keeping up with these changes has started to make itself felt in recent years. In the study, the technical stages of digital artworks created by using contentious producer networks from deep learning algorithms were discussed and evaluated within the scope of intellectual and artistic works law. In the study, 6989 abstract and portrait paintings, which are a subset of the Wiki-Art dataset, were used. As a result, it has been seen that the number of images in the dataset affects the originality of the outputs. It is thought that the proposed method can be applied to different branches of art and can give art lovers a different perspective.

Keywords

Artworks, Generative Adversarial Networks, End-to-End, Artificial Intelligence

References

• Akmeşe, Ö. F., 2022. Diagnosing Diabetes with Machine Learning Techniques. Hittite Journal of Science and Engineering, 9(1), 9-18.
• Alaskar, H., & Saba, T., 2021. Machine Learning and Deep Learning: A Comparative Review. Proceedings of Integrated Intelligence Enable Networks and Computing: IIENC 2020, 143-150.
• Aslan, O., Gunal, S., & Dincer, B. T., 2018. A computational morphological lexicon for turkish: Trlex. Lingua, 206, 21-34.
• Chen, H., Zhao, L., Qiu, L., Wang, Z., Zhang, H., Xing, W., & Lu, D., 2020. Creative and diverse artwork generation using adversarial networks. IET Computer Vision, 14(8), 650-657.
• Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y., 2014. Generative adversarial networks 2014. arXiv preprint arXiv:1406.2661, 1406.
• Hayit, T., Erbay, H., Varçın, F., Hayit, F., & Akci, N., 2021. Determination of the severity level of yellow rust disease in wheat by using convolutional neural networks. Journal of Plant Pathology, 103(3), 923-934.
• Hunt, E. B., 2014. Artificial intelligence. Academic Press.
• Mazzone, M., & Elgammal, A., 2019. Art, creativity, and the potential of artificial intelligence. In: Arts. MDPI, 8(1).
• Terman, L. M., 1948. The measurement of intelligence, 1916.
• Turhan, C.G., & Bilge, H.Ş., 2020. Scalable image generation and super resolution using generative adversarial networks. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(2).
• Radford, A., Metz, L., & Chintala, S., 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.
• Roziere, B., Teytaud, F., Hosu, V., Lin, H., Rapin, J., Zameshina, M., & Teytaud, O., 2020. Evolgan: Evolutionary generative adversarial networks. In Proceedings of the Asian Conference on Computer Vision.
• Saravanan, R., & Sujatha, P., 2018,. A state of art techniques on machine learning algorithms: a perspective of supervised learning approaches in data classification. In 2018 Second international conference on intelligent computing and control systems (ICICCS), IEEE, 945-994.
• Shahriar, S., 2022. GAN computers generate arts? a survey on visual arts, music, and literary text generation using generative adversarial network. Displays, 102237.
• Wason, R., 2018. Deep learning: Evolution and expansion. Cognitive Systems Research, 52, 701-708.
• Xue, A., 2021. End-to-end chinese landscape painting creation using generative adversarial networks. In Proceedings of the IEEE/CVF Winter conference on applications of computer vision, 3863-3871.
• Zhou, Z. H., 2021. Machine learning. Springer Nature.
• https://www.wikiart.org/ (07.03.2023)