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This paper presents a novel approach for guiding a Generative AdversarialNetwork trained on the FashionGen dataset to generate designs corresponding totarget fashion styles. Finding the latent vectors in the generator's latentspace that correspond to a style is approached as an evolutionary searchproblem. A Gaussian mixture model is applied to identify fashion styles basedon the higher-layer representations of outfits in a clothing-specific attributeprediction model. Over generations, a genetic algorithm optimizes a populationof designs to increase their probability of belonging to one of the Gaussianmixture components or styles. Showing that the developed system can generateimages of maximum fitness visually resembling certain styles, our approachprovides a promising direction to guide the search for style-coherent designs.

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