Autoencoders for Representation Learning
Encoder-decoder architecture, bottleneck, reconstruction loss, applications in anomaly detection.
What is an Autoencoder?
An autoencoder learns to compress data into a small code and reconstruct it back. It has two halves: an encoder that compresses, and a decoder that rebuilds. The squeeze in the middle forces it to learn what matters.
import torch
import torch.nn as nnThe Bottleneck Idea
The narrow middle layer is the bottleneck or latent code. By forcing all information through a few numbers, the network must discard noise and keep the essential structure of the data.
All lessons in this course
- Autoencoders for Representation Learning
- Variational Autoencoders (VAE)
- GANs: Generator and Discriminator
- Conditional GANs and Style Transfer