  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.
