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Autoencoders

PreviousData GenerationNextEvaluating Neural Networks

Last updated 3 years ago

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An Autoencoder (AE) is a Neural Network that can be trained in an Unsupervised fashion (basically Self Supervised). In an AE the Output values are equal to the Inputs. Autoencoders are used to reduce the size of a set of inputs, by generating a more compact representation. The AE consists of an Encoder that compress the data and a Decoder that reconstruct the data from this compressed representation.

Sound related

The Official Keras tutorial about Autoencoders
A Gentle introduction to Autoencoders
Using AE to remove noise
Convolutional AE and more
More advanced topics with AE
Introduction to sparce AE and PCA vs AE comparison
Training VAEs
Introduction to Magenta Variational Autoencoder