Normalizing Flows Explained | Flow Matching Part-1 | Generative AI

In this tutorial video, we dive deep into Normalizing Flows - both explanation and implementation. We’ll begin with why normalizing flows are important when we already have VAEs and GANs in generative modeling. Once we have understood the motivation, we will get into what normalizing flows are, starting with the foundation behind flow-based models which is - Change of Variables Theorem for probability densities. As part of understanding change of variables theorem for multi dimensional cases, we’ll explore the role of the Jacobian in normalizing flows. At this point we would have the understanding that normalizing flows are just modelling single transformations and now from modelling a single function, we move to using normalizing flows to model compositions of invertible functions, enabling us to convert simple distributions to complex ones with decent success. As an example of a deep generative model using the normalizing flow technique, we will cover Real NVP paper but focusing mainly on affine coupling layers rather than entire paper. Finally, we’ll walk through a step-by-step PyTorch implementation of a RealNVP-like model on the MNIST dataset and look at the results we get. ⏱️ Timestamps 00:00 Introduction 00:25 Why Normalizing Flows 02:22 Change of Variables Theorem Explained 11:46 Role of Jacobian in Normalizing Flows 15:30 Composition of invertible functions 19:32 Design Constraints of a Normalizing Flows model 21:09 Real NVP and Affine Coupling Layers 29:18 PyTorch implementation of a Normalizing Flows model(RealNVP like) 39:58 Results 📖 Resources Github Implementation Link(to be uploaded soon) - https://github.com/explainingai-code/Normalizing-Flow 🔔 Subscribe: https://tinyurl.com/exai-channel-link Email - explainingai.official@gmail.com