What are GANs ? | Introduction to Generative Adversarial Networks | Face Generation & Editing - 30

Key Takeaways

have you ever seen those creepy viral videos with human faces transforming into other people or animals and ask to yourself how is this done then here is the video you waited for let's stive into the subject Gans or generative adversarial networks is a recent class of machine learning frameworks that was first introduced by Ian Goodfellow and his colleagues in 2014 it is a powerful approach to generated modeling using deep learning methods such as convolutional neural networks generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original data set cans are a clever way of training a generative model by framing the problem as a supervised learning problem with two sub models the generator model that we train to generate new examples and the discriminator model that tries to classify examples as either real from the domain or fake generated the two models are trained together in a zero-sum game adversarial until the discriminator model is fooled about half the time meaning the generator model is generating plausible examples in this case zero-sum means that when the discriminator successfully identifies real and fake samples it is rewarded or no change is needed to the model parameters whereas the generator is penalized with large updates to model parameters alternately when the generator fools the discriminator it is rewarded or no change is needed to the model parameters but the discriminator is penalized and its model parameters are updated we can think of the generator as being like a counterfeiter trying to make fake money and the discriminator as being like police trying to allow legitimate money and catch counterfeit money to succeed in this game the counterfeiter must learn to make money that is indistinguishable from genuine money and the generator network must learn to create samples that are drawn from the same distribution as the training data because Lagaan framework can naturally be analyzed with the tools of game theory we call Gans adversarial but what are their usage well they are used in a lot of different applications one is their ability to generate high resolution versions of input images create new and artistic images sketches painting and more it has the ability to translate photographs across domains such as day to night summer to winter and more Gans have been able to generate photos so realistic that humans are unable to tell that they are of objects scenes and people that do not exist in real life there are many research reasons why Gans are interesting important and require further study Gans open a lot of exciting opportunities since its generating data when you don't have access to a lot and requires no human supervision which is ideal for deep learning applications please leave a like if you learn something and subscribe to the channel to not miss any terms clearly explained [Music] [Music]