Understanding Variational Autoencoders (VAEs) | Deep Learning скачать в хорошем качестве

Understanding Variational Autoencoders (VAEs) | Deep Learning

Here we delve into the core concepts behind the Variational Autoencoder (VAE), a widely used representation learning technique that uncovers the hidden factors of variation throughout a dataset. Timestamps -------------------- Introduction 0:00 Latent variables 01:53 Intractability of the marginal likelihood 05:08 Bayes' rule 06:35 Variational inference 09:01 KL divergence and ELBO 10:14 ELBO via Jensen's inequality 12:06 Maximizing the ELBO 12:57 Analyzing the ELBO gradient 14:34 Reparameterization trick 15:55 KL divergence of Gaussians 17:40 Estimating the log-likelihood 19:04 Computing the log-likelihood 19:58 The Gaussian case 20:17 The Bernoulli case 21:56 VAE architecture 23:33 Regularizing the latent space 25:37 Balance of losses 28:00 Useful links ------------------------ Original VAE paper: https://arxiv.org/abs/1312.6114 More detailed explanation: https://arxiv.org/abs/1906.02691 Nice discussion of the reparameterization trick: https://gregorygundersen.com/blog/201... Intro to variational inference and the ELBO: https://www.cs.cmu.edu/~epxing/Class/... On the problem of learnt variance in the decoder: https://arxiv.org/abs/2006.13202 VAE tutorial in Keras: https://keras.io/examples/generative/... MIT lecture on deep generative modelling:    • MIT 6.S191 (2023): Deep Generative Modeling   Deriving the KL divergence for Gaussians: https://leenashekhar.github.io/2019-0... Article with a nice discussion of regularized latent spaces: https://towardsdatascience.com/unders...