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The rise of deep learning applications in the fashion industry has fueledadvances in curating large-scale datasets to build applications for productdesign, image retrieval, and recommender systems. In this paper, the authorproposes using Variational Autoencoder (VAE) to build an interactive fashionproduct application framework that allows the users to generate products withattributes according to their liking, retrieve similar styles for the sameproduct category, and receive content-based recommendations from othercategories. Fashion product images dataset containing eyewear, footwear, andbags are appropriate to illustrate that this pipeline is applicable in thebooming industry of e-commerce enabling direct user interaction in specifyingdesired products paired with new methods for data matching, and recommendationsystems by using VAE and exploiting its generated latent codes.

Exploiting Latent Codes: Interactive Fashion Product Generation, Similar  Image Retrieval, and Cross-Category Recommendation using Variational  Autoencoders