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Research Article
NFTGAN: Non-Fungible Token Art Generation Using Generative Adversarial Networks
Javagar M1
Sanjeeth S2
Dr.J.B.Jona3
S.A.Gunasekaran4
12 Integrated Master’s in Decision and Computing Sciences, Coimbatore Institute of Technology, Tamil Nadu, India. 3Associate Professor, Department of Computer Application, Coimbatore Institute of Technology, Tamil Nadu, India. 4Assistant Professor, Department of Computer Application, Coimbatore Institute of Technology, Tamil Nadu, India.
Published Online: May-June 2022
Pages: 532-536
Cite this article
No DOIReferences
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10.1162/leon_a_02003.
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[10] L. Kang, P. Riba, Y. Wang, M. Rusiñol, A. Fornés, and M. Villegas, “GANwriting: Content-Conditioned Generation of
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58592-1_17.
Press, 2012.
[2]. J. Ponciano, “Solana Skyrockets To New High—Amassing Nearly $40 Billion In Market Value As Competition With Ethereum Heats
Up,” Forbes. https://www.forbes.com/sites/jonathanponciano/2021/08/31/solana-skyrockets-to-new-high-above-130-amassing-nearly
40-billion-in-market-value-as-competition-with-ethereum-heats-up/ (accessed Dec. 06, 2021).
[3]. L. Kugler, “Non-Fungible Tokens and the Future of Art,” Commun ACM, vol. 64, no. 9, pp. 19–20, Aug. 2021,
doi: 10.1145/3474355.
[4]. A. Brown, “Beeple NFT Sells For $69.3 Million, Becoming Most-Expensive Ever,”
Forbes.https://www.forbes.com/sites/abrambrown/2021/03/11/beeple-art-sells-for-693-million-becoming-most-expensive-nft-ever/
(accessed Dec. 06, 2021).
[5]. Q. Wang, R. Li, Q. Wang, and S. Chen, “Non-fungible token (NFT): Overview, evaluation, opportunities and challenges,” ArXiv Prepr.
ArXiv210507447, 2021.
[6]. L.-H. Lee et al., “All one needs to know about metaverse: A complete survey on technological singularity, virtual ecosystem, and
research agenda,” ArXiv Prepr. ArXiv211005352, 2021.
[7]. L.-H. Lee et al., “When Creators Meet the Metaverse: A Survey on Computational Arts,” ArXiv Prepr. ArXiv211113486, 2021.
M. Franceschet et al., “Crypto Art: A Decentralized View,” Leonardo, vol. 54, no. 4, pp. 402–405, Aug. 2021, doi:
10.1162/leon_a_02003.
[8]. G. H. de Rosa and J. P. Papa, “A survey on text generation using generative adversarial networks,” Pattern Recognit., vol. 119, p.
108098, Nov. 2021, doi: 10.1016/j.patcog.2021.108098.
[9]. S. Frolov, T. Hinz, F. Raue, J. Hees, and A. Dengel, “Adversarial text-to-image synthesis: A review,” Neural Netw., vol. 144, pp. 187–
209, Dec. 2021, doi: 10.1016/j.neunet.2021.07.019.
[10] L. Kang, P. Riba, Y. Wang, M. Rusiñol, A. Fornés, and M. Villegas, “GANwriting: Content-Conditioned Generation of
Styled Handwritten Word Images,” in Computer Vision – ECCV 2020, Cham, 2020, pp. 273–289. doi: 10.1007/978-3-030-
58592-1_17.
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