Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space

Imke Grabe*, Jichen Zhu*, Manex Aguirrezabal Zabaleta

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

2 Citations (Scopus)

Abstract

This paper presents a novel approach for guiding a Generative Adversarial Network trained on the FashionGen dataset to generate designs corresponding to target fashion styles. Finding the latent vectors in the generator’s latent space that correspond to a style is approached as an evolutionary search problem. A Gaussian mixture model is applied to identify fashion styles based on the higher-layer representations of outfits in a clothing-specific attribute prediction model. Over generations, a genetic algorithm optimizes a population of designs to increase their probability of belonging to one of the Gaussian mixture components or styles. Showing that the developed system can generate images of maximum fitness visually resembling certain styles, our approach provides a promising direction to guide the search for style-coherent designs.
Original languageEnglish
Title of host publicationInternational Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar)
Place of PublicationSpringer, Cham
Publication date2022
Pages84-100
Publication statusPublished - 2022

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