Variational Autoencoder - VISUALLY EXPLAINED!
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3 سال پیش
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This tutorial provides an in-depth
This tutorial provides an in-depth explanation of 3 big but related ideas in machine learning - Latent Variable Model, Amortized Inference and Variational Autoencoder.
Most of the time VAE is explained as an Autoencoder where the latent vector has a distribution however that explanation misses the main goals behind its motivation.
First, we see what it means to have an amortized inference and how a certain category of models called Latent Variable Models requires it in order to be efficient when we deal with large datasets.
Then we construct a neural network that addresses the challenges with Latent Variable Models leading to the creation of VAE.
Recommended videos to watch before this one
KL Divergence
KL Divergence - CLEARLY EXPLAINED!
Evidence Lower Bound
Evidence Lower Bound (ELBO) - CLEARLY...
#variationalinference
#latentvariable
#variationalautoencoder
Most of the time VAE is explained as an Autoencoder where the latent vector has a distribution however that explanation misses the main goals behind its motivation.
First, we see what it means to have an amortized inference and how a certain category of models called Latent Variable Models requires it in order to be efficient when we deal with large datasets.
Then we construct a neural network that addresses the challenges with Latent Variable Models leading to the creation of VAE.
Recommended videos to watch before this one
KL Divergence
KL Divergence - CLEARLY EXPLAINED!
Evidence Lower Bound
Evidence Lower Bound (ELBO) - CLEARLY...
#variationalinference
#latentvariable
#variationalautoencoder
3 سال پیش
در تاریخ 1400/10/03 منتشر شده
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