An Autoencoder is an unsupervised neural network that learns to compress data into a lower-dimensional representation (encoding) and then reconstruct the original data from that representation (decoding). Think of it as learning to create a compact summary of data that still contains all the essential information. Autoencoders are used for dimensionality reduction, denoising, anomaly detection, and generating new data.
Watch data compress through the bottleneck and reconstruct
Data is compressed in the middle, then reconstructed to match the original
The magic of autoencoders is the bottleneck layer. By forcing the network to compress data through a narrow bottleneck, it must learn the most important features. It can't simply memorize the input—it must learn meaningful patterns and structure. This compressed representation (latent code) captures the essence of the data in just a few numbers. It's like explaining a complex story in a single sentence—you keep only what matters most!
Training an autoencoder to remove noise from photos:
Input: Noisy image (256×256×3 = 196,608 pixels)
Encoder: Conv layers reduce to 128 → 64 → 32 → 16 features
Bottleneck: Just 256 numbers capture the essential image
Decoder: Transpose conv layers expand back: 16 → 32 → 64 → 128
Output: Clean reconstructed image (256×256×3)
Result: Noise is filtered out because it's not in the learned representation!
1. Forward Pass (Encoding): Input → Encoder → Latent code (compressed representation)
2. Forward Pass (Decoding): Latent code → Decoder → Reconstruction
3. Calculate Loss: Compare reconstruction to original input (MSE, BCE)
4. Backpropagation: Update weights to minimize reconstruction error
5. Iterate: Repeat until reconstructions match originals closely
6. Result: Learned compressed representation that preserves key features
Latent Space: Discrete points
Purpose: Compression and reconstruction
Generation: Can't generate new samples
Training: Minimize reconstruction loss only
Latent Space: Probability distributions
Purpose: Generation + reconstruction
Generation: Sample new points from distribution
Training: Reconstruction + KL divergence loss
Imagine compressing a 1000-page book into a 1-page summary:
Too large (500 pages): Can include lots of details, but haven't really compressed
Just right (1 page): Must capture only the key plot points and themes
Too small (1 word): Loses too much information, can't reconstruct the story
The bottleneck size is crucial: large enough to preserve information, small enough to learn
meaningful features. This is the art of autoencoder design!