Generative Adversarial Network to Evaluate the Ceramic Art Design through Virtual Reality with Augmented Reality
Keywords:
Ceramic Art, Deep Learning, Generative Adversarial Networks, Reinforcement Learning, Augmented Reality (AR), Virtual reality (VR)Abstract
A ceramic art exhibition is a curated display of ceramic artworks, sculptures, pottery, and other ceramic creations. These exhibitions provide a platform for ceramic artists to showcase their work and allow art enthusiasts, collectors, and the public to appreciate the beauty and diversity of ceramic art. Ceramic art exhibitions have embarked on a dynamic transformation by harnessing the power of augmented reality (AR) and virtual reality (VR) technology. This paper proposed an architecture of the GAN-RL (Generative Adversarial Networks with Reinforcement Learning) process. the fusion of AR and VR technologies with augmented flow networks generated by the GAN-RL process. The GAN-RL framework introduces adaptability and interactivity to the visitor's journey, shaping their experience based on preferences and real-time feedback. With the proposed GAN-RL the ceramic art designs are evaluated and trained in the network for the analysis. Finally, the GAN-designed features are applied in the deep learning model for the classification process. The results demonstrated that the deep learning model with the GAN-RL achieves an accuracy of 0.99 which is significantly higher compared with the conventional techniques.
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