This objective is achieved using an Adversarial loss. The goal is to learn a mapping function G: X-> Y such that images generated by G(X) are indistinguishable from the image of Y. a transformation between winter image and summer imageįaceApp is one of the most popular examples of CycleGAN where human faces are transformed into different age groups.Īs an example, let’s say X is a set of images of horse and Y is a set of images of zebra.a transformation between images of horse and zebra,.a map between artistic and realistic images,.As you see in Fig1 and Fig2 that when a data point from the training dataset is given as input to the Discriminator, it calls it out as a Real sample whereas it calls out the other data point as fake when it’s generated by the Generator.įig4: Objective function in GAN formulationĬycleGAN is a very popular GAN architecture primarily being used to learn transformation between images of different styles.Īs an example, this kind of formulation can learn: Samples generated by the Generator is termed as a fake sample. The Generator generates synthetic samples given a random noise and the Discriminator is a binary classifier that discriminates between whether the input sample is real or fake. GAN comprises of two independent networks, a Generator, and a Discriminator. Let’s discuss the core concepts of GAN formulation. Once the model is successfully trained, you can sample new, “generated” observations that follow the training distribution. The primary objective of the Generative Model is to learn the unknown probability distribution of the population from which the training observations are sampled from. Our topic of discussion, Generative Adversarial Networks(GANs) is an example of the Generative Model. On the other side, Generative Models are primarily used to generate synthetic data points that follow the same probability distribution as training data distribution. Discriminative Models are primarily used to solve the Classification task where the model usually learns a decision boundary to predict which class a data point belongs to. There are 2 kinds of models in the context of Supervised Learning, Generative and Discriminative Models. In this article, we will talk about some of the most popular GAN architectures, particularly 6 architectures that you should know to have a diverse coverage on Generative Adversarial Networks (GANs). Generating very high-resolution images (ProgressiveGAN) and many more.Generating an image from a textual description (text-to-image),.Transforming an image from one domain to another (CycleGAN),.Some of the most popular GAN formulations are: In recent times, it started showing promising results in Audio, Text as well. GANs has shown tremendous success in Computer Vision. With the invention of GANs, Generative Models had started showing promising results in generating realistic images. You have CycleGAN, followed by BiCycleGAN, followed by ReCycleGAN and so on. Within a few years, the research community came up with plenty of papers on this topic some of which have very interesting names :). and since then this topic itself opened up a new area of research. Generative Adversarial Networks (GANs) were first introduced in 2014 by Ian Goodfellow et.
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