A Gentle Introduction to Autoencoders & Latent Space

Autoencoders are a type of unsupervised neural network used to transform high-dimensional input into a compact, lower-dimensional representation. The architecture consists of an encoder to compress data, a bottleneck layer containing the latent space representation, and a decoder to reconstruct the original data. Training is accomplished by minimizing the reconstruction loss between the input and the reconstructed output, which implicitly forces the encoder to learn a meaningful, compressed representation. Besides data compression, autoencoders have applications in image denoising, inpainting, and object removal, though they can suffer from generating blurry results when using a simple MSE loss.