VQ-VAEs - Neural Discrete Representation Learning - Paper + PyTorch Code Explained

Explore a comprehensive 35-minute video tutorial on VQ-VAEs (Vector Quantized Variational Autoencoders) and their application in neural discrete representation learning. Delve into the key concepts of autoencoders and VAEs before examining the motivation behind discrete representations. Gain a high-level understanding of the VQ-VAE framework, followed by an in-depth exploration of its components, including the VQ-VAE loss function. Study the PyTorch implementation and discuss the missing KL term. Investigate prior autoregressive models and analyze the results. Conclude with an introduction to VQ-VAE-2, highlighting its hierarchical structure of latents and priors. Access supplementary resources, including research papers and code examples, to enhance your understanding of this important concept in AI and machine learning.