Understanding the Variational Autoencoder (VAE) Model: A Theoretical Overview and Implementation

In this blog post, we explored the inner workings of the Variational Autoencoder (VAE) model. We started by understanding that VAE is a generative model that estimates the Probability Density Function (PDF) of the training data, allowing it to generate new examples similar to the dataset it was trained on.

We discussed the challenges of modeling images due to the dependencies between pixels and the need for a latent space where essential information for generating images resides. The VAE model aims to find a latent vector that describes an image and can be used to generate new images.

To train the VAE model, we introduced the concept of Variational Inference and the use of the reparameterization trick to deal with intractable distributions. By maximizing the likelihood of the data and minimizing the Kullback–Leibler divergence, the model can learn to generate new images based on the learned distribution.

Overall, the VAE model involves encoding an input image, sampling a latent vector, decoding it into an image, and optimizing the model to reconstruct images accurately and maintain a similarity between the learned distribution and the prior distribution.

In the upcoming post, we will provide a working code of a VAE model trained on the MNIST dataset of handwritten digits, demonstrating how to generate new digit images. Stay tuned for more on VAE implementation and practical examples!