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12— Generative Adversarial Networks (GANs)

We start today with a deep dive into the DarkNet architecture used in YOLOv3, and use it to better understand all the details and choices that you can make when implementing a resnet-ish architecture. The basic approach discussed here is what we used to win the DAWNBench competition!

Then we’ll learn about Generative Adversarial Networks (GANs). This is, at its heart, a different kind of loss function. GANs have a generator and a discriminator that battle it out, and in the process combine to create a generative model that can create highly realistic outputs. We’ll be looking at the Wasserstein GAN variant, since it’s easier to train and more resilient to a range of hyperparameters.