More talks in the program:
11:30 - 12:15
Arguably one of the most beautiful ideas in the Deep Learning revolution of the past decade has been the invention of Generative Adversarial Networks (GANs). This new architecture allows a neural network to learn the distribution of the training data, allowing for the generation of new data samples, entirely unsupervised. While this idea was initially shown to work on small, gray-scale images, the past few years have brought tremendous progress to the domain of GANs both in terms of training algorithms, model architectures and computational scale. The sample quality of modern networks has now reached the point where generated samples are almost indistinguishable from real ones, opening an entirely new era of digital media creation. In this talk I will shed light on: how a GAN actually works and how GANs relate to similar ideas like auto-encoders and auto-regressive models. I will also take a look at the current state of the art of sample generation in both images, audio and other data types, and take an overview of the emerging industry surrounding this novel tool for digital media creation.