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?