Lecture
This lecture covers deep generative models, focusing on Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). VAEs aim to learn latent representations and generate new data by sampling from the learned distribution. GANs consist of a generator and a discriminator, where the generator aims to produce realistic samples to fool the discriminator. The lecture discusses the training challenges, weaknesses, and potential solutions for GANs, such as Wasserstein GANs. It also explores other generative models like DALL-E, which can create images from text descriptions. The session concludes with a demonstration of various generative models and their applications.