Auto_encoder
An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. Along with the reduction side, a reconstructing side is learnt, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input, hence its name. Several variants exist to the basic model, with the aim of forcing the learned representations of the input to assume useful properties. Examples are the regularized autoencoders (Sparse, Denoising and Contractive autoencoders), proven effective in learning representations for subsequent classification tasks, and Variational autoencoders, with their recent applications as generative models.Autoencoders are effectively used for solving many applied problems, from face recognition to acquiring the semantic meaning of words.
Image of Auto Encoder
Applications
- Dimensionality Reduction
- Relationship with principal component analysis (PCA)
- Information Retrieval
- Anomaly Detection
- Image Processing
- Drug discovery
- Machine Translation
- Population synthesis