Welcome to the world of GAN! - Nerd Platoon Welcome to the world of GAN! - Nerd Platoon

Take a look at these people..

Let me tell you a secret , these people don’t exist at all , it’s neither photoshop nor a manual digital art by a human . If your mind boggled for a while , Welcome to the world of GAN! Now, take a look at these arts . As you might have already guessed these arts are also not made by human artists at all. All credit goes to GAN . Try generating some at thispersondoesnotexist and  nightcafe.






In this blog post, we will be discussing the generative adversarial network (GAN), a machine learning algorithm that has recently become very popular in the field of artificial intelligence.Wikipedia defines it as,”Generative adversarial network (GAN) is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in June 2014. Two neural networks contest with each other in a game (in the form of a zero-sum game, where one agent’s gain is another agent’s loss)”. GAN is a specific kind of neural network designed to produce high-quality results through the use of two opposing networks. While traditional neural networks rely on raw data to train their models, the GAN instead trains its model through an iterative process in which one network tries to predict the output of the other. Since this model only relies on past data and not actual new information, it is often able to produce new and never-before-seen images. Here, we will take a look at the basic concept of the GAN, as well as the ways in which it can be used to solve real-world problems.

 

The GAN is a type of deep neural network that has many applications in the field of machine learning. A neural network is essentially a mathematical model that consists of a number of interconnected units known as “neurons”. These neurons are loosely inspired by the biological neuron found in the human brain; they each take an input in the form of a set of values and produce an output in the form of a value or set of values. The main benefit of using a neural network over a traditional rule-based system is that it can learn to perform complex tasks using a small amount of training data. Many different algorithms have been developed over the years for executing this type of training, each with its own strengths and weaknesses. One algorithm in particular that has become very popular recently is the GAN.

How are the networks trained?

The basic concept behind the GAN is quite simple: there are two competing neural networks, referred to as the generator and the discriminator. The generator network attempts to produce fake input samples using some arbitrary probability distribution function; these samples can then be used by the discriminator network to distinguish the real data from the fake data. This process is repeated over and over until the generator network is able to produce fake data that looks realistic to the discriminator network. If the real data looks like the fake data produced by the generator network, then the training is complete; otherwise, the process must be repeated until a satisfactory result is obtained

 

Future of GAN

While still relatively new, GANs have been widely hailed as the future of artificial intelligence. Applications of GANs include image generation, speech recognition, music generation, natural language processing, and machine translation. With all these applications becoming possible, it seems that we are only at the beginning of this exciting new field. However, there is also some concern about the future of GANs. For example, what happens if a GAN turns evil and starts producing terrorist propaganda? Or what if an autonomous car crashes due to an error in a GAN’s self-driving software? Deep fakes have already started causing some cyber crime issues. Clearly, we need to address the risks of this technology and develop systems that can be used safely in the future.

 

Challenges

There are several challenges that need to be overcome in order for GANs to be successfully used in real-world applications. One challenge has to do with the lack of diversity in the data used to train a GAN. When the training data consists of a small set of images and audio clips from a single group of people, it is difficult to produce a model capable of reproducing images that are realistic and diverse enough to fool the discriminator. A second challenge is dealing with the training time required for a large GAN network. As the size of the network increases, the training time also increases exponentially. This means that it will take a very long time to train a very large GAN network using current hardware. Finally, another big challenge is the fact that most datasets used for training GANs are labeled by humans and are therefore unreliable. A more accurate method would be to automatically label the data using computer vision techniques.