Hello there! This is my story of making a GAN that would generate images of cars, with PyTorch.

First of all, let me tell you what a GAN is — at least to what I understand what it is.

A Generative Adversarial Network(GAN) is a network we use to generate something(image, sound… anything), What we challenge here is the ability of the machine to imagine something. (For the paragraph below know that a GAN has two networks: A Generator and a Discriminator.)

How do we make a machine imagine something?

Let’s say we’re trying to make the machine imagine some form of data, just as usual, we’ll start with it(our generator) imagining random noise — data that doesn’t make any sense at all.

We then feed it to another network that is trained specifically to distinguish between fake and real data(our discriminator), this network enables us to say how fake the generated data is, knowing which we’ll update the generation process to make it more and more real over training.

Also note that we’ll be training our discriminator at the same time too(of course, we freeze the generator for a second), to distinguish better between real and fake images as our generator gets better.

You can imagine it to be similar to two people playing a game, one person knows a target picture that the other has to draw, and the other just draws pictures, seeing the picture drawn, the first person gives the second some feedback on how close his picture looks to the target, based off which he makes changes and gets better and better towards an ideal picture.

In the simplest terms, thats how it works.(feel free to correct me though)

#machine-learning #deeplearing #pytorch #image-generation #deep learning