Generative adversarial networks (GANs) are deep neural net architectures comprised of two nets, pitting one against the other (thus the “adversarial”).
GANs were introduced in a paper by Ian Goodfellow and other researchers at the University of Montreal, including Yoshua Bengio, in 2014. Referring to GANs, Facebook’s AI research director Yann LeCun called adversarial training “the most interesting idea in the last 10 years in ML.”
GANs’ potential is huge, because they can learn to mimic any distribution of data. That is, GANs can be taught to create worlds eerily similar to our own in any domain: images, music, speech, prose.
One neural network, called the generator, generates new data instances, while the other, the discriminator, evaluates them for authenticity; i.e. the discriminator decides whether each instance of data it reviews belongs to the actual training dataset or not.
Let’s say we’re trying to do something more banal than mimic the Mona Lisa. We’re going to generate hand-written numerals like those found in the MNIST dataset, which is taken from the real world. The goal of the discriminator, when shown an instance from the true MNIST dataset, is to recognize them as authentic.
Meanwhile, the generator is creating new images that it passes to the discriminator. It does so in the hopes that they, too, will be deemed authentic, even though they are fake. The goal of the generator is to generate passable hand-written digits, to lie without being caught. The goal of the discriminator is to identify images coming from the generator as fake.
The generator takes in random numbers and returns an image. This generated image is fed into the discriminator alongside a stream of images taken from the actual dataset. The discriminator takes in both real and fake images and returns probabilities, a number between 0 and 1, with 1 representing a prediction of authenticity and 0 representing fake. So you have a double feedback loop:
The discriminator is in a feedback loop with the ground truth of the images, which we know. The generator is in a feedback loop with the discriminator.
In this project I used a special dataset containing many images and trained a GAN to generate images like them . Thus infusing Computation with creativity .
real sample :
generated sample after 24th epoch :
