An autoencoder is a type of artificial neural network that learns to compress input data into a lower-dimensional representation (encoding) and then reconstruct it back to its original form (decoding). The network consists of two main components: an encoder that reduces data dimensionality through a bottleneck layer, and a decoder that attempts to reconstruct the original input from this compressed representation.
Autoencoder Architecture
The autoencoder architecture processes data through multiple neural network layers, with the encoder reducing dimensionality until reaching the bottleneck layer, and the decoder expanding it back to the original dimensions. Training minimizes the reconstruction error between input and output, forcing the network to learn efficient data representations. Common variants include denoising autoencoders, variational autoencoders (VAEs), and sparse autoencoders, each optimized for specific use cases.
Autoencoders are primarily used for dimensionality reduction, feature learning, and anomaly detection. In computer vision, they perform image compression and denoising tasks. In security applications, they detect network intrusions and fraudulent transactions. They are also employed in recommendation systems for learning user preferences, and in healthcare for analyzing medical imaging data and genetic information.
Learn more about autoencoders, encoders and decoders in Lilian Weng's amazing blog post.
