2 Variational Autoencoders (VAEs)

This chapter covers

The core principles of autoencoders and how they learn to compress and reconstruct data
The architecture and functioning of Variational Autoencoders (VAEs) and their ability to generate new data
The Beta-VAE (β-VAE) as an extension of the standard VAE and its implications for disentangled representation learning

In the rapidly evolving field of computer vision, autoencoders have emerged as powerful tools that bridge the gap between data compression and generative modeling. This chapter dives into the fascinating world of autoencoders, exploring their fundamental concepts, advanced variants, and Generative AI applications.

2.1 Introduction to Autoencoders

Autoencoders are a class of neural networks that aim to learn efficient representations of data in an unsupervised manner. The core idea behind autoencoders is deceptively simple — train a network to reconstruct its own input. However, this simple premise leads to powerful applications in dimensionality reduction, feature learning, and generative modeling.

At a high level, an autoencoder consists of two main components:

An encoder network that compresses the input data into a lower-dimensional representation.
A decoder network that attempts to reconstruct the original input from this compressed representation.

2.1.1 The Latent Space

2.1.2 Applications of Autoencoders

2.1.3 Autoencoder Architecture and Training

2.2 Implementing an Autoencoder in PyTorch

2.2.1 Step 1: Import Necessary Libraries

2.2.2 Step 2: Prepare the Dataset

2.2.3 Step 3: Implement the Autoencoder Model

2.2.4 Step 4: Define the Loss Function and Optimizer

2.2.5 Step 5: Train the Autoencoder

2.2.6 Step 6: Model Evaluation

2.2.7 Conclusion and Next Steps

2.3 Variational Autoencoders (VAEs)

2.3.1 VAE Model Architecture: Concepts and Components

2.3.2 The Loss Function and VAE Training

2.4 Implementing VAE in PyTorch

2.4.1 Step 1: Import Necessary Libraries

2.4.2 Step 2: Prepare the Dataset

2.4.3 Step 3: Implement the Encoder

2.4.4 Step 4: Implement the Decoder

2.4.5 Step 5: Implement the VAE Model

2.4.6 Step 6: Define the VAE Loss Function

2.4.7 Step 7: Initialize the Model and Optimizer

2.4.8 Step 8: Train the VAE

2.4.9 Step 9: Model Evaluation

2.4.10 Latent Space Interpolation with VAE

2.5 β-VAE and Latent Space Disentanglement

2.5.1 Applications and Implications of β-VAEs

2.6 Conclusion

2.7 Summary