Abstract Art, a highly popular artistic genre, often serves as a canvas for expressing the artist's emotions. Numerous researchers have endeavoured to analyse abstract art through the application of machine and deep learning techniques, focusing on tasks such as edge detection, brushstroke analysis, and emotion recognition. This research paper presents an investigation of a wide distribution of abstract paintings using Generative Adversarial Neural-Networks(GANs). 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 proposed mDCGAN incorporates meticulous adjustments in layer configurations, offering tailored optimisation techniques and loss functions to effectively combat issues like mode collapse and gradient vanishing in order to improve stability and realism in art generation. The evaluation results of mDCGAN demonstrates a remarkable reduction in mode collapse occurrences when compared to the standard DCGAN configuration. 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.
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