• PATEL TARUNKUMAR KISHANBHAI
• Dhaval A. Parikh

Generating images from natural language is one of the primary applications of recent conditional generative models. Besides testing our ability to model conditional, highly dimensional distributions, text to image synthesis has many exciting and practical applications such as photo editing or computer-aided content creation. Recent progress has been made using Generative Adversarial Networks (GANs). This material starts with a gentle introduction to these topics and discusses the existent state of the art models so here vast majority of prior work for this problem focus on how to increase the resolution of low-resolution images which are artificially generated by simple bilinear down-sampling (or in a few cases by blurring followed by down-sampling) and Decrease Loss Function.

Image Synthesis , Super-resolution, Generative Adversarial Networks, GANs.

"Images Synthesis And Image Degradations Using Generative Adversarial Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.550-556, April-2019, Available :http://www.jetir.org/papers/JETIR1904B83.pdf

"Images Synthesis And Image Degradations Using Generative Adversarial Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp550-556, April-2019, Available at : http://www.jetir.org/papers/JETIR1904B83.pdf