International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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Using Generative Adversarial Network (GAN) to Produce Artistic painting Anushka Naik1, Amogh Sanzgiri2 1 Student, Dept. Of Information Technology, Goa College of Engineering, Goa, India
2 Professors, Dept. of Information Technology, Goa College of Engineering, Goa, India
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Abstract - This paper uses Generative Adversarial
with their ability to generate realistic and novel content, present a unique opportunity to explore traditional artistic styles with the computational power of modern technology. [5] GAN has two components and works like a game based model. A Generator (G) produces the sample and Discriminator (D) tries to distinguish between G’s produced sample and original painting. If D is successful in identifying fake then a penalty is imposed on G and if D is not successful in identifying fake then a penalty is imposed on D.Due to this penalty the generator and discriminator learn and improve its performances.
Networks (GANs) to create an antique Indian painting style using JPEG photos, emulating the manner of the well-known Indian artist Raja Ravi Varma. In order to bridge the gap between artificial intelligence and creative expression, the goal is to investigate the potential of GANs to produce aesthetically pleasing and stylistically rich artworks. Using a carefully chosen dataset, the GAN architecture is trained as part of the approach, enabling the model to pick up on the complex compositions, textures, and patterns specific to his painting genres. By use of the antagonistic interaction between a discriminator and generator, the GAN aims to generate artworks that combine elements of computational creativity with conventional creative approaches. The project's output has the potential to be used in content development, digital art creation, and the democratization of artistic tools. This initiative, which uses GANs, adds to the field of generative art by offering a forum for the fusion of artificial intelligence and human creativity.
Though GAN have evolved over time but their area of research is limited in particular domain. Most GAN have focused on western art but ignored importance of Asian especially Indian art style. One of the many resons behind this is less dataset availibility and its diversity due to various painters. The culture depicting the art changes from place to place even though the artform may be the same.
Key Words: Generative Adversarial Netwok (GAN), artistic style, Raja Ravi Verma, AI painting, CycleGAN, StyleTransfer
2. RELATED WORK 2.1 Generative Adversarial Network (GAN)
1. INTRODUCTION
Generative adversarial networks (GANs), introduced by Goodfellow[8] , is an emerging technology for both unsupervised and semi-supervised learning. They are implicit density generative models, and they are characterized by two main components: a generator G, and a discriminator D. The basic idea of GANs is to set up a game between the generator and discriminator. The former tries to generate samples that are intended to come from the real data distribution, while the latter examines real and generated samples in order to distinguish between real or fake data. A common analogy is to think of the generator as an art forger, and the discriminator as an art expert. The forger tries to create forgeries which are increasingly similar to real paintings, in order to deceive the art expert. The expert, at the same time, learns more and more sophisticated ways to discriminate between real and false artworks. One of the most crucial points of GANs is that the generator has no direct access to the real data: the only manner for it to learn is through interaction with the discriminator. By contrast, the discriminator has access to both real and generated data. This behavior can be expressed via a min max game, where the generator tries
Artistic expression has long been a reflection of cultural identity, creativity, and the evolving narrative of human civilization. It has helped us to connect our past with present and highlighting our evolution, importance and dominance over time. Ajanta and Ellora caves is one such significant evidence. But with time art and their artists are disappearing not due to advancement in technology but also due to its technique. Traditional painting technique is time consuming, presence of artist physically and unavailability of raw materials. Indeed, high resolution cameras, small storage devices and fast printing machines have made people shift their interest from traditional art form. Though with such existing problems the craze for traditional art is still among us and is growing over time . In such case there is much need to preserve our traditional art by using modern techniques such as Generative Adversarial Network (GAN). The advent of Generative Adversarial Networks (GANs)[8] has revolutionized the field of artificial intelligence, particularly in the domain of creativity. GANs,
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