Abstract:
"Fashion designers encounter a lot of difficulties when crafting and designing in-vogue fashion designs.
As a result, many fashion-design productions don’t come large-scale to the market and are below
standard. These problems affect the demand and supply chain of the fashion design industry.
These problems can be addressed with the usage of text-to-image synthesis. Text-to-image synthesis is
the process of transforming text descriptions into high-quality two-dimensional images. After critically
analyzing the existing systems, text descriptions with a large number of words that transform into
images have not yet been identified in the text-to-image synthesis domain. So, the author has decided
to bridge the gap by building a novel algorithm using Attn generative adversarial networks ensembled
with a contrastive learning approach to synthesize fashion-design-based descriptions to high-quality
fashion designs.
This system is developed using deep learning, following a multi-level architecture of GAN networks.
An image-text encoder is simulated and trained to emphasize the words provided in a text description
and make it semantically consistent with the images. Additionally, during training, contrastive loss of
the image and text is computed to minimize the distance of textual descriptions related to the same
image and maximize those related to different images. an Attn GAN network is employed to train the
text description. After training a maximum of 800 epochs, the GAN model was able to generate images
for a variety of text descriptions for classes (Shirts, Trousers, Blazers, Shorts & Tops and Dresses).
Also, with the use of ESRGAN trained on the FashionGen dataset, the final image that was generated
was of good resolution."
