Open AccessImage Synthesis Based On Feature Description
Author(s) -
Rohan Bolusani
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.37812
Subject(s) - generative grammar , discriminative model , computer science , artificial intelligence , feature (linguistics) , field (mathematics) , adversarial system , artificial neural network , bridge (graph theory) , deep learning , image (mathematics) , natural language processing , machine learning , pattern recognition (psychology) , linguistics , medicine , philosophy , mathematics , pure mathematics
Generating realistic images from text is innovative and interesting, but modern-day machine learning models are still far from this goal. With research and development in the field of natural language processing, neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, in the field of machine learning, generative adversarial networks (GANs) have begun to generate extremely accurate images of especially in categories, such as faces, album covers, and room interiors. In this work, the main goal is to develop a neural network to bridge these advances in text and image modelling, by essentially translating characters to pixels the project will demonstrate the capability of generative models by taking detailed text descriptions and generate plausible images. Keywords: Deep Learning, Computer Vision, NLP, Generative Adversarial Networks