This video discusses generative models beyond GANs. It focuses on minimizing the Kullback-Leibler divergence between the generated and training data distributions. A key challenge is calculating the log-likelihood, often intractable for complex models. Variational Autoencoders (VAEs) address this by maximizing a lower bound (ELBO), decomposing it into reconstruction and regularization terms. VAEs learn two networks: a generative model and an encoder mapping data to latent variable distributions. Extensions like β-VAE and VQ-VAE improve interpretability and resolution, respectively, though VAEs generally achieve lower performance than GANs, but offer easier training.