Generative Models

AutoEncoder

Introduced by Hinton et al. in Reducing the Dimensionality of Data with Neural Networks

An Autoencoder is a bottleneck architecture that turns a high-dimensional input into a latent low-dimensional code (encoder), and then performs a reconstruction of the input with this latent code (the decoder).

Image: Michael Massi

Source: Reducing the Dimensionality of Data with Neural Networks

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Anomaly Detection 49 7.98%
Clustering 36 5.86%
Denoising 32 5.21%
Time Series 24 3.91%
General Classification 20 3.26%
Dimensionality Reduction 15 2.44%
Image Generation 11 1.79%
Unsupervised Anomaly Detection 10 1.63%
Image Classification 10 1.63%

Components


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

Categories