From Autoencoder to Beta-VAE

Lilian Weng · 7 years ago

Autoencoders use neural networks with bottleneck layers to reconstruct high-dimensional data while compressing it into lower-dimensional latent representations. The bottleneck layer typically reduces data dimensionality significantly, enabling applications in search, compression, and factor discovery. This compressed representation allows systems to learn underlying patterns in data that can be applied across multiple downstream tasks.