Autoencoder
An Autoencoder is a type of unsupervised neural network designed to learn efficient data codings (representations) by training the network to ignore signal noise. It consists of an encoder that compresses the input data, and a decoder that reconstructs the input from the compressed representation.
Frequently Asked Questions
What is the bottleneck in an autoencoder?▼
The bottleneck is the middle layer that contains the compressed representation (latent space) of the input data.
Can autoencoders be used for generative AI?▼
Yes, Variational Autoencoders (VAEs) are generative variants that enforce a specific probability distribution on the latent space.
Quick Facts
- CategoryNeural Architectures
- Key ApplicationDimensionality reduction, image denoising, and anomaly detection
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