Autoencoders and Representation Learning in Vision

Autoencoders are neural networks that compress data into a lower-dimensional space and reconstruct the original input, learning non-linear structures unlike linear PCA. Their two-stage design features an encoder that projects input data into a latent space to extract informative features.