Dimensionality Reduction

Autoencoders

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.

Extracted from: Wikipedia

Image source: Wikipedia

Papers


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Components


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🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

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