A hands-on lab exploring the implementation and application of autoencoders using Keras. We will cover shallow and deep architectures for tasks like image denoising, compression, and de-blurring.
This lab provides a comprehensive walkthrough of building and utilizing autoencoders, a powerful type of unsupervised neural network. We will start by constructing a simple, shallow autoencoder using different Keras APIs and then move on to powerful, real-world applications.
As the figure below illustrates, autoencoders work by first encoding an input image into a compressed, lower-dimensional representation (latent space) and then decoding it back to its original form. This compressed data is incredibly useful, achieving dimensionality reduction while preserving the image's most important features. 🧠
This lab is broken down into three main parts:
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Part 1: Building a Shallow Autoencoder
- We will first review and implement a basic autoencoder.
- You will learn to build the same model using both the flexible Keras Functional API and the fully customizable Model Sub-classing approach.
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Part 2: Applications of Autoencoders
- Image Denoising: Train an autoencoder to take a noisy image as input and output a clean, reconstructed version.
- Image Compression: Use the output of the encoder layer to get a compressed representation of an image and visualize the quality of the reconstruction.
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Part 3: Building Deep & Convolutional Autoencoders
- We will use the concepts from the previous sections to build more powerful, deep autoencoders.
- We will then implement a Convolutional Autoencoder (CAE) and compare its performance against the deep (fully-connected) version.
A key conclusion from this lab is the superior performance of Convolutional Autoencoders for image data. We demonstrate that because a CAE can effectively learn complex spatial features, it can be used to de-blur blurred images and represent features much more clearly than a standard deep autoencoder.