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I have read many papers that recommend using Variational Autoencoders over Autoencoders since they have a more probabilistic approach and the ability to use KL divergence on the latent dimension. But when trying to test both networks I find that the variability of the output in Variational Autoencoders is reducing the accuracy of the network and I am getting better results when using Autoencoders. I am still working on very simple data and training my network on normal images that do not have any augmentation or changing background.

  • Does the performance of Variational Autoencoders increase with harder data or is there any other reason to choose it over Autoencoders?
  • Or do Autoencoders perform better in anomaly detection?
Stephen Rauch
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Jack Farah
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1 Answers1

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Variational autoencoders encourage the model to generalize features and reconstruct images as an aggregation of those features. This is what the latent space encodes, a compressed feature vector.

Vanilla autoencoders memorize the input and map to the output without the generalization. If you want to extrapolate from your dataset, variational is the way to go.

neurokinetikz
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