Handwritten Digits Image Generation with help of Generative Adversarial Network: Machine Learning Approach

In recent years, research into Generative Adversarial Nets (GANs) has increased dramatically. GAN was first proposed in 2014 and has since used in various real-time applications,Includes computer vision and natural language processing for approximately accurate results. Image composition is the most popular study of the many applications of GAN,Studies in this area have already shown the great future of using GAN for image composition.This article shows how to classify image composition methods, reviews different models of text-to-image composition and image-to-image conversionand provides some metrics and future research on image composition using GAN. I will explain the direction of in this paper. In current years, frameworks using Generative Adversarial Networks (GAN) have been very successful in many areas many areas, especially in image generation, asthey can create very realistic and crisp images and train on large datasets. However, successful GAN training can be very difficult if you need high resolution images. Text-to-image compositing, image-to-image conversion, face manipulation, 3D image compositing, and deep master printing are five interesting areas that can be applied to image compositing based on the state-of-the-art GAN technology described in this article. It presents a comprehensive analysis of current GAN-based imaging models, including their strengths and weaknesses. At the same time, recent rediscovery of deep learning and widespread interest in generation methods in the scientific community have made it possible to generate realistic images by learning the data distribution from noise. If the input data contains information about the visual content of the image, the quality of the generated image will improve.