Generative Models

Denoising Autoencoder

A Denoising Autoencoder is a modification on the autoencoder to prevent the network learning the identity function. Specifically, if the autoencoder is too big, then it can just learn the data, so the output equals the input, and does not perform any useful representation learning or dimensionality reduction. Denoising autoencoders solve this problem by corrupting the input data on purpose, adding noise or masking some of the input values.

Image Credit: Kumar et al


Paper Code Results Date Stars


Task Papers Share
Denoising 151 37.56%
General Classification 10 2.49%
Test 8 1.99%
Anomaly Detection 7 1.74%
Imputation 6 1.49%
Dimensionality Reduction 6 1.49%
Time Series Analysis 6 1.49%
Language Modelling 6 1.49%
Domain Adaptation 6 1.49%


Component Type
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