Auto-Encoding Variational Bayes (VAE Paper Summary)
Summary of Kingma & Welling's foundational VAE paper introducing the reparameterization trick and variational …...
Generative Modeling
Importance Weighted Autoencoders (IWAE Paper Summary)
Summary of Burda, Grosse & Salakhutdinov's ICLR 2016 paper introducing Importance Weighted Autoencoders for tighter …...
Importance Weighted Autoencoders: Beyond the Standard VAE
The key difference between multi-sample VAEs and IWAEs: how log-of-averages creates a tighter bound on log-likelihood.
A Contrastive Learning Approach for Training Variational Autoencoder Priors
Aneja et al.'s NeurIPS 2021 paper introducing Noise Contrastive Priors (NCPs) to address VAE's 'prior hole' problem with …...
Modern PyTorch Techniques for VAEs: A Hands-On Tutorial
Learn to implement VAEs in PyTorch: ELBO objective, reparameterization trick, loss scaling, and MNIST experiments on …
GAN Objective Functions: A Comprehensive Guide
Complete guide to GAN objective functions including WGAN, LSGAN, Fisher GAN, and more. Understand which loss function to …
Understanding Generative Adversarial Networks (GANs)
Learn about GANs with intuitive explanations and mathematical foundations. Learn how adversarial networks generate …