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Auto Encoders
Last updated: 1/9/2025
Questions
Notes
Autoencoders are types of neural networks that comrpess the data into a latent space that can be used to reconstruct the previous data.
Types
Denoising autoencoder
It is an autoencoder that takes in a noisy image and then outputs a "cleaned" version of the image.
Variational autoencoders
These are types of autoencoders that are better at generating data because the latent space is smaller and more clustered.
They have a higher reconstruction loss compared to regular autoencoders, but they are more fault tolerant (their latent space can be represented with less precision).
You can use VAE's to increase the size of your dataset.
VqVAEs
Applications
It can be used to generate more examples in supervised learning (i.e. make more training data)
See Also
- [[sigaida]]