Manuscript Number : IJSRST205846

Survey on Text to Image Synthesis

Authors(5) :-Chaitanya Ghadling, Firosh Vasudevan, Ruchin Dhama, Shreya Lad, Dr. Sunil Rathod

One of the most difficult things for current Artificial Intelligence and Machine Learning systems to replicate is human creativity and imagination. Humans have the ability to create mental images of objects by just visualizing and having the general looks description of that particular object. In recent years with the evolution of GANs (Generative Adversarial Network) and its gaining popularity for being able to somewhat, replicate human creativity and imagination, research on generating high quality images from text description is boosted tremendously. Through this research paper, we are trying to explore various GANs architectures to develop a model to generate plausible images of birds from detailed text descriptions with visual realism and semantic accuracy.

Authors and Affiliations

Chaitanya Ghadling
Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India
Firosh Vasudevan
Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India
Ruchin Dhama
Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India
Shreya Lad
Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India
Dr. Sunil Rathod
Professor, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India

GAN, AI, ML

  1. AttnGAN: Fine - grained Text to Image Generation with Attentional Generative Adversarial Networks.
  2. StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks.
  3. Generative Adversarial Text to Image Synthesis.
  4. https://en.wikipedia.org/wiki/Generative_adversarial_network
  5. https://paperswithcode.com/task/text-to-image-generation

Publication Details

Published in : Volume 5 | Issue 8 | November-December 2020
Date of Publication : 2020-12-18
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 270-272
Manuscript Number : IJSRST205846
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Chaitanya Ghadling, Firosh Vasudevan, Ruchin Dhama, Shreya Lad, Dr. Sunil Rathod, " Survey on Text to Image Synthesis", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 5, Issue 8, pp.270-272, November-December-2020.
Journal URL : https://ijsrst.com/IJSRST205846
Citation Detection and Elimination     |      | |https://en.wikipedia.org/wiki/Generative_adversarial_network
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    • " target="_blank">RIS | https://en.wikipedia.org/wiki/Generative_adversarial_network
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