Autoencoders and Representation Learning in Vision

Autoencoders are a type of neural network that compress data into a lower-dimensional space and then reconstruct the original input from that compressed representation. If you've ever encountered Principal Component Analysis (PCA), then you already have an intuition for how this works. The key difference is that PCA is a linear projection method, while autoencoders use neural networks, allowing them to learn non-linear structure in the data. In theory, a linear autoencoder with a single hidden l