The Application and Exploration of Data Visualization Technology in Artistic Expression and Exhibition

Abstract

Embarking on the transformation of virtual art museums, the confluence of data visualization technology and deep learning emerges as a catalyst for redefining artistic expression. Driven by the desire to change traditional approaches, this work utilizes a sophisticated deep learning model, the self-attention-based cycle-consistent generative adversarial network (SA-CCGAN), for transformative purposes. The objective of proposed Modernizing Virtual Art: The Collaboration of Data Visualization Technology and Self-Attention-Based Cycle-Consistent Generative Adversarial Network for Artistic Expression (MVA: DVT & SA-CCGAN) is to elevate artistic expression and exhibition practices within the virtual space, marking a paradigm shift in traditional approaches. SA-CCGAN, renowned for its ability to generate realistic and coherent artistic representations, serves as the cornerstone of this transformative endeavor. The workflow intricately incorporates SA-CCGAN into the virtual art museum context, enhancing stylized outcomes and capturing global geometric features. This yields a computationally efficient representation of high-dimensional visual data, revolutionizing digital art presentation. The fusion of data visualization technology, creativity, and meticulous design not only signals innovation but also has the potential to redefine the virtual art landscape, fostering accessibility, engagement, and a deeper cultural exchange in digital art exploration.