A Tiered GAN Approach for Monet-Style Image Generation

TL;DR

This paper presents a tiered GAN framework that progressively refines Monet-style images, improving artistic quality and addressing common GAN training issues, with promising results but room for further enhancement.

Contribution

Introduces a multi-stage tiered GAN architecture for artistic image generation that enhances quality and stability over traditional single-stage GANs.

Findings

Successfully generates Monet-style images with detailed structures

Addresses training instability and mode collapse issues

Demonstrates potential for high-quality artistic synthesis

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.