A Tiered GAN Approach for Monet-Style Image Generation

TL;DR

The study tackles the challenge of generating Monet-style artwork with GANs by introducing a tiered, multi-stage architecture that progressively refines outputs from random noise. It combines downsampling and convolutional techniques to enable high-quality artistic generation while maintaining computational efficiency, and it systematically analyzes GAN limitations in artistic generation to propose improvements. Experimental results show foundational artistic structures emerging across stages, but realism and faithful capture of Monet's style require larger, more diverse data and further architectural refinements. The work lays a scalable framework for progressive image synthesis in art, with concrete plans for dataset expansion, distributed training, and transfer learning to bridge the gap to true Monet-style fidelity.

Abstract

Generative Adversarial Networks (GANs) have proven to be a powerful tool in generating artistic images, capable of mimicking the styles of renowned painters, such as Claude Monet. This paper introduces a tiered GAN model to progressively refine image quality through a multi-stage process, enhancing the generated images at each step. The model transforms random noise into detailed artistic representations, addressing common challenges such as instability in training, mode collapse, and output quality. This approach combines downsampling and convolutional techniques, enabling the generation of high-quality Monet-style artwork while optimizing computational efficiency. Experimental results demonstrate the architecture's ability to produce foundational artistic structures, though further refinements are necessary for achieving higher levels of realism and fidelity to Monet's style. Future work focuses on improving training methodologies and model complexity to bridge the gap between generated and true artistic images. Additionally, the limitations of traditional GANs in artistic generation are analyzed, and strategies to overcome these shortcomings are proposed.

Figures (8)

• Figure 1: An overview of how GAN model works
• Figure 2: A tiered GAN model: Transforming random input to refined output. The left image displays the initial random noise, and the right image shows the refined image. This highlights the model's ability to enhance and transform images effectively.
• Figure 3: Different steps of Monet GAN Model
• Figure 4: Generator and Discriminator Architecture
• Figure 5: Progressive Refinement of Image Generation through Multiple Stages. $G$ begins with random noise (M3) and undergoes iterative enhancements across four GAN models, culminating in the final high-quality output (MF). Each stage represents a significant improvement in detail and quality, transitioning from M3 to M2, then to M1, before achieving the final image.