Generative Modeling with Variational Auto Encoder (VAE)
Understanding the intuition behind Variational Autoencoder
The generative model is one of the interesting fields in machine learning where the network is trained to learn the data distribution which then can be used to generate new content instead of classifying data. The two approaches most commonly used for generative modeling are Generative Adversarial Network (GAN) and Variational Autoencoder (VAE). In this article, I will attempt to explain the intuition behind Variational Autoencoder (VAE) and how it can generate data like the faces above.
Auto Encoder (AE)
Before going to Variational Autoencoder, we will first discuss Auto Encoder. Auto Encoder is a self-supervised neural network that learns how to encode the input into lower dimensions, then decode and reconstruct the data again to be as close as the input as efficiently as possible.
Autoencoder consists of 3 parts:
- Encoder, the layers that encode the input data into a lower dimension representation.
- Compressed, the layer that contains the…
