Understanding GANs (Generative Adversarial Networks) | Deep Learning

GANs use an elegant adversarial learning framework to generate high quality samples of everything from images to audio. Here, we explore the theoretical underpinnings, as well as some practical problems that can plague training, such as non-convergence and mode collapse. Timestamps -------------------- 00:00 Introduction 01:28 Generative modelling 04:46 The GAN approach 07:37 Loss function 12:14 Game theory perspective 13:18 Optimal discriminator 15:33 Optimal generator 17:26 Training dynamics 19:45 Optimal discriminator problem 21:39 Training steps 22:13 Non-convergence 23:39 Mode collapse Links -------- - Original GAN paper (https://arxiv.org/abs/1406.2661) - Analysis of vanishing/unstable gradients (https://arxiv.org/abs/1701.04862) - Analysis of mode collapse (https://arxiv.org/abs/1606.03498) - Wasserstein GAN paper (https://arxiv.org/abs/1701.07875) - Keras CGAN tutorial (https://keras.io/examples/generative/conditional_gan/) - PyTorch DCGAN tutorial (https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html)