Text2FashionGAN: Augmenting Personalized Style Recommendations with cGANs and Word2Vec

Generative Adversarial Networks (GANs) have taken on a significant role in the fashion industry, influencing how designers develop and how consumers interact with fashion. This research introduces an innovative application that uses GANs and word embedding techniques to show the importance of GANs in the fashion industry to create fashion illustrations based on written descriptions. First, the significance of word embedding for understanding the linguistic connections between words is discussed. Then, GANs' significance in the fashion industry is explained, highlighting their function in trend prediction and creativity. Next, the novel method, Text2FashionGAN which uses GAN technology and word embedding to create fashion images from text inputs is explained. Text2FashionGAN helps people find trends that suit their tastes while enabling designers to easily generate ideas by bridging the text-to-image gap. With the revolutionary potential of GANs and the practicality of textual descriptions, this method revolutionizes fashion recommendations for both designers and consumers.