Generative Adversarial Networks (GANs) were first introduced in 2014 by Ian Goodfellow et. Within a few years, the research community came up with plenty of papers on this topic some of which have very interesting names:). You have CycleGAN, followed by BiCycleGAN, followed by ReCycleGAN and so on. With the invention of GANs, Generative Models had started showing promising results in generating realistic images. GANs has shown tremendous success in Computer Vision.
Machine learning is an ever-evolving field, so it can be easy to feel like you're out of the loop on the latest developments changing the world this week. One of those emerging areas that have been getting a lot of buzz lately is GANs--or generative adversarial networks. So to keep you in the machine learning loop, we've put together a short crash course on GANs: With generative models, the aim is to model the distribution of a given dataset. For the generative models that we're talking about today, that dataset is usually a set of images, but it could also be other kinds of data, like audio samples or time-series data. There are two ways to go about getting a model of this distribution: implicitly or explicitly.
There has been a large resurgence of interest in generative models recently (see this blog post by OpenAI for example). These are models that can learn to create data that is similar to data that we give them. The intuition behind this is that if we can get a model to write high-quality news articles for example, then it must have also learned a lot about news articles in general. Or in other words, the model should also have a good internal representation of news articles. We can then hopefully use this representation to help us with other related tasks, such as classifying news articles by topic.
Generative Adversarial Networks, or GANs for short, are a deep learning technique for training generative models. The study and application of GANs are only a few years old, yet the results achieved have been nothing short of remarkable. Because the field is so young, it can be challenging to know how to get started, what to focus on, and how to best use the available techniques. In this crash course, you will discover how you can get started and confidently develop deep learning Generative Adversarial Networks using Python in seven days. Note: This is a big and important post. You might want to bookmark it. How to Get Started With Generative Adversarial Networks (7-Day Mini-Course) Photo by Matthias Ripp, some rights reserved.