We have previously looked at autoencoders constructed with fully-connected layers. While useful for various data types, these architectures can encounter difficulties when processing image data due to its high dimensionality and inherent spatial structure. Flattening images into vectors for standard autoencoders often results in the loss of important local relationships between pixels.

To address these challenges with image data, this chapter focuses on Convolutional Autoencoders (ConvAEs). These models integrate principles from Convolutional Neural Networks (CNNs), allowing them to effectively process images by preserving spatial information and learning hierarchical features.

In this chapter, you will learn to:

• Recognize why fully-connected autoencoders are often suboptimal for image data.
• Use convolutional layers in the encoder for feature map generation and pooling layers for spatial down-sampling.
• Employ transposed convolutional layers and other upsampling techniques in the decoder to reconstruct images.
• Design and build a complete Convolutional Autoencoder model architecture.
• Understand how ConvAEs extract meaningful, hierarchical features from images.
• Implement a ConvAE for feature extraction from an image dataset in a practical, hands-on session.