GANs : Making Fakes Look Real
What is a GAN? GANs or Generative Adversarial Networks were invented by Ian Goodfellow [1] in the year 2014. In a GAN architecture two neural networks are in an adversarial role (therefore the name adversarial network), where we have a generator and a detector or a discriminator. The generator attempts to create a dataset such that the discriminator isn't able to detect if the data is real (from any given distribution) or it is fake (generated by the generator). The generator is given a random initial input or noise from which it learns to generate the data, this generated data is then fed into the discriminator, where we simultaneously input real-data. The discriminator here generates a label (in reality it is a probability distribution) for the fake (or the generated data from generator) and the real dataset. A basic flow can be seen in the following figure : GAN Arch. Source: [2] How Does it Really Learn ? For any type of learning to take place we need to decide an objective