MIT 6.S191: Introduction to Deep Learning - MIT 6.S191 (2024): Deep Generative Modeling

MIT Introduction to Deep Learning 6.S191: Lecture 4 Deep Generative Modeling Lecturer: Ava Amini 2024 Edition For all lectures, slides, and lab materials: http://introtodeeplearning.com​ Lecture Outline 0:00​ - Introduction 6:10- Why care about generative models? 8:16​ - Latent variable models 10:50​ - Autoencoders 17:02​ - Variational autoencoders 23:25 - Priors on the latent distribution 32:31​ - Reparameterization trick 34:36​ - Latent perturbation and disentanglement 37:40 - Debiasing with VAEs 39:37​ - Generative adversarial networks 42:09​ - Intuitions behind GANs 44:57 - Training GANs 48:28 - GANs: Recent advances 50:57 - CycleGAN of unpaired translation 55:03 - Diffusion Model sneak peak Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!