Fake Image Generator

Abstract

Generative Adversarial Network which is popularly known as GANs is a deep learning, unsupervised machine learning technique which is proposed in year 2014. The main blocks of this architecture are; Generator: This block tries to generates the images which are very similar to that of original dataset by taking noise as input. It tries to learn the join probability of the input data (X) and output data(Y); P(X|Y). Discriminator: This block tries to accept two inputs, one from main dataset and other from images generated from Generator, bifurcates them as Real or Fake. To make this Generative and Adversarial process simple, both these blocks are made from Deep Neural Network based architecture which can be trained through forward and backward propagation techniques. From the time GANs were introduced, there has been tremendous advancement in the GANs. There are GAN architecture which are specifically made for some tasks. We heard the news on Artistic Style Transfer and face-swapping applications (aka deepfakes), Natural Voice Generation (Google Duplex), Music Synthesis, smart reply, smart compose, etc. The technology behind these kinds of AI is called a GAN, or “Generative Adversarial Network”. A GAN takes a different approach to learning than other types of neural networks. GANs algorithmic architectures that use two neural networks called a Generator and a Discriminator, which “compete” against one another to create the desired result. The Generator’s job is to create realisticlooking fake images, while the Discriminator’s job is to distinguish between real images and fake images. If both are functioning at high levels, the result is images that are seemingly identical real-life photos. Generative Adversarial Networks have had a huge success since they were introduced in 2014 by Ian J. Goodfellow and co-authors in the article Generative Adversarial Nets.