Variational Autoencoders

A lecture that discusses variational autoencoders. We discuss generative models, plain autoencoders, the variational lower bound and evidence lower bound, variational autoencoder architecture, and stochastic optimization of the variational lower bound. This lecture is from Northeastern University's CS 7150 Summer 2020 class on Deep Learning, taught by Paul Hand. The notes are available at: http://khoury.northeastern.edu/home/hand/teaching/cs7150-summer-2020/Variational_Autoencoders.pdf References: Kingma and Welling 2019: Kingma, Diederik P., and Max Welling. "An Introduction to Variational Autoencoders." Foundations and Trends® in Machine Learning 12, no. 4 (2019): 307-392. https://arxiv.org/abs/1906.02691 Kingma and Welling 2014: Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013). Razavi et al. 2019: Razavi, Ali, Aaron van den Oord, and Oriol Vinyals. "Generating diverse high-fidelity images with VQ-VAE-2." In Advances in Neural Information Processing Systems, pp. 14866-14876. 2019.