Module 3: AutoEncoders and Generative Models
Module Overview
This module introduces AutoEncoders, a class of neural networks designed for unsupervised learning and dimensionality reduction. Unlike the previously covered neural network architectures that focused on prediction tasks, autoencoders learn to compress data into a lower-dimensional representation and then reconstruct it, extracting meaningful features in the process.
You'll explore both standard autoencoders and their powerful extension, Variational AutoEncoders (VAEs), which enable generative capabilities. By the end of this module, you'll understand how to implement autoencoders for tasks like image reconstruction and information retrieval, opening up applications in recommendation systems, anomaly detection, and data generation.
Learning Objectives
1. Describe the components of an autoencoder
- Identify the encoder and decoder components of an autoencoder
- Explain the purpose and architecture of the latent space
- Distinguish between different types of autoencoders (standard, variational, etc.)
- Understand the loss functions used in training autoencoders
2. Apply an autoencoder to an image reconstruction problem
- Implement autoencoder models for image data using Keras
- Train autoencoders to restore damaged or noisy images
- Evaluate reconstruction quality using appropriate metrics
- Compare different autoencoder architectures for image tasks
3. Apply an autoencoder to a basic information retrieval problem, i.e. a "recommender system"
- Understand how latent representations can be used for information retrieval
- Implement similarity measures in the latent space
- Build a simple recommender system using autoencoder representations
- Explore applications of autoencoders in semantic search and recommendation
Guided Project
AutoEncoders and Generative Models
Project Resources
Guided Project File:
DS_433_Autoencoders_Lecture.ipynb
Module Assignment
Please read the assignment file in the GitHub repository for detailed instructions on completing your assignment tasks.
Assignment File:
DS_433_Autoencoders_Assignment.ipynb
In this assignment, you will work with two types of autoencoders through two main exercises:
- Exercise 1: Apply autoencoders to restore damaged images
- Load and prepare the MNIST dataset with artificial noise
- Build a convolutional autoencoder model for image restoration
- Train the model to reconstruct clean images from noisy inputs
- Evaluate and visualize the restoration results
- Exercise 2: Explore Variational Autoencoders (VAEs)
- Understand the theory and architecture of VAEs
- Implement a VAE model with encoder and decoder components
- Explore the latent space and understand KL divergence loss
- Generate new digit images by sampling from the learned latent space
- Visualize label clusters in the latent space
Assignment Solution Video
Check for Understanding
Complete the following items to test your understanding:
- Explain the key differences between standard autoencoders and variational autoencoders
- Describe how the bottleneck layer in an autoencoder helps with feature learning
- Explain the purpose of the KL divergence loss in variational autoencoders
- Discuss how autoencoders can be used for anomaly detection
- Compare and contrast discriminative models and generative models in machine learning