Definition
Autoencoder is a type of Neural Network used for unsupervised dimensionality reduction or feature extraction. It consists of two main parts: encoder and decoder. Encoder compresses the input data into a lower-dimensional representation, and decoder attempts to reconstruct the original input from the compressed representation. Once autoencoder is trained, decoder is no longer used.
Architecture
The model is trained by minimizing the reconstruction error, typically using mean squared error
Facts
The extracted features may be used to train other supervised models.
Autoencoder can be used for anomaly detection task. Typical examples have low reconstruction error, whereas outliers should have high reconstruction error.