Abstract
Abstract Art is an immensely popular, discussed form of art that often has the ability to depict the emotions of an artist. Many researchers have made attempts to study abstract art in the form of edge detection, brush stroke and emotion recognition algorithms using machine and deep learning. This papers describes the study of a wide distribution of abstract paintings using Generative Adversarial Neural Networks(GAN). GANs have the ability to learn and reproduce a distribution enabling researchers and scientists to effectively explore and study the generated image space. However, the challenge lies in developing an efficient GAN architecture that overcomes common training pitfalls. This paper addresses this challenge by introducing a modified-DCGAN (mDCGAN) specifically designed for high-quality artwork generation. The approach involves a thorough exploration of the modifications made, delving into the intricate workings of DCGANs, optimisation techniques, and regularisation methods aimed at improving stability and realism in art generation enabling effective study of generated patterns. The proposed mDCGAN incorporates meticulous adjustments in layer configurations and architectural choices, offering tailored solutions to the unique demands of art generation while effectively combating issues like mode collapse and gradient vanishing. Further this paper explores the generated latent space by performing random walks to understand vector relationships between brush strokes and colours in the abstract art space and a statistical analysis of unstable outputs after a certain period of GAN training and compare its significant difference. These findings validate the effectiveness of the proposed approach, emphasising its potential to revolutionise the field of digital art generation and digital art ecosystem.
Abstract (translated)
抽象艺术是一种非常受欢迎且备受讨论的绘画形式，通常具有描绘艺术家情感的能力。许多研究者使用机器和深度学习尝试研究抽象艺术，以边缘检测、笔触和情感识别算法的形式。本文描述了使用生成对抗网络（GAN）对广泛分布的抽象绘画进行研究。GAN具有学习和复制分布的能力，使研究人员和科学家能够有效探索和研究生成的图像空间。然而，挑战在于开发一个高效的GAN架构，克服常见的训练陷阱。本文通过引入一种专门为高质量艺术品生成而设计的modified-DCGAN（mDCGAN）来解决这一挑战。该方法深入探讨了所做修改，深入研究了DCGAN的复杂工作原理、优化技术和正则化方法，旨在提高艺术生成过程中的稳定性和真实性，从而有效研究生成模式。所提出的mDCGAN在层配置和架构选择方面进行了细致的调整，为艺术生成提供了针对性的解决方案，同时有效地解决了诸如模式收缩和梯度消失等问题。此外，本文通过进行随机的漫步来探索抽象艺术空间中笔触和颜色之间的向量关系，并对训练过程中一定期限内的不稳定输出进行统计分析，比较其与显著差别的差异。这些发现证实了所提出方法的有效性，强调了其对数字艺术生成领域和数字艺术生态系统的变革潜力。
URL
https://arxiv.org/abs/2403.18397
