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.

翻译：抽象艺术是一种极为流行且备受讨论的艺术形式，往往能够描绘艺术家的情感。许多研究者已尝试利用机器学习和深度学习，通过边缘检测、笔触识别和情感识别算法研究抽象艺术。本文描述了使用生成对抗网络（GAN）对抽象画作的广泛分布进行的研究。GAN能够学习并复现数据分布，使研究者和科学家能够有效探索并研究生成的图像空间。然而，挑战在于开发一种能够克服常见训练缺陷的高效GAN架构。本文通过引入一种专门用于高质量艺术作品生成的改进型DCGAN（mDCGAN）来解决这一挑战。该方法深入探讨了所做的改进，详细剖析了DCGAN的内部工作机制、优化技术以及旨在提升生成艺术稳定性与真实性的正则化方法，从而实现对生成模式的有效研究。提出的mDCGAN在层配置和架构选择上进行了精细调整，为艺术生成的独特需求提供了定制化解决方案，同时有效应对了模式坍塌和梯度消失等问题。此外，本文通过在抽象艺术空间中进行随机游走探索生成的潜在空间，以理解笔触与颜色之间的向量关系，并对GAN训练一定周期后的不稳定输出进行统计分析，比较其显著差异。这些发现验证了所提方法的有效性，强调了其在革新数字艺术生成领域及数字艺术生态系统方面的潜力。