Exploring AI-Powered Image Generation for Fashion Collaborations: A Deep Learning Approach to Blending Shoe Designs from Multiple Brands

Abstract

This thesis presents an approach for fashion shoe design generation using Generative Adversarial Networks (GANs) with a focus on the WGAN-GP, ProGAN, and StyleGAN architectures. The primary objective is to assess the visual quality, diversity, and adherence to the fashion trends of the generated shoe designs. A set of evaluation metrics, including Fréchet Inception Distance (FID), Earth Mover’s Distance (EMD), Maximum Mean Discrepancy (MMD), and K-Nearest Neighbours (KNN) accuracy, are employed to quantitatively evaluate the performance of each model. The evaluation results demonstrate that ProGAN outperforms both WGAN-GP and StyleGAN in all metrics, achieving the lowest FID, EMD, and MMD scores. ProGAN generates visually appealing and diverse shoe designs, that closely resembling real-world fashion trends. While WGAN-GP also achieves acceptable results, StyleGAN faces challenges with the droplet effect and noisy colours in the generated designs, resulting in lower performance in all evaluation metrics. Nevertheless, StyleGAN’s style-mixing capability showcases its potential for creating novel and creative shoe designs. The qualitative evaluation further confirms the ProGAN as the preferred choice for generating high-quality and fashion-forward shoe designs. Its robustness, progressive growing approach, and architectural stability contribute to its outstanding performance. The discussion also highlights the potential implications of GAN-based fashion image generation in the fashion industry and creative domains, such as trend forecasting and virtual garment display. The findings suggest that ProGAN holds promise as a reliable and creative tool for generating fashion-forward shoe designs and adopting StyleGAN 2 with the Noise-Conditional AdaIN (NCAIN) layer may address the droplet effect problem and enhance visual coherency in the generated designs. Ultimately, the results and discussions pave the way for further advancements and applications of GAN-based fashion design in the fashion industry and creative domains. Keywords: Deep learning, shoe design, GANs, style-mixing.