[D] Autoencoders vs VAE for semi-supervised learning
I hope to clear a few doubts regarding Variational Autoencoders.
According to the above paper, the representations learned by VAEs are used for semi-supervised learning. However, in my experiments I was getting better accuracy results with regular autoencoders than VAEs. I do know that we can generate new samples using a VAE but is there a reason why VAEs are used in the paper instead of regular autoencoders? What can be the advantages of the representations of VAE compared to that of an autoencoder w.r.t. semi-supervised learning?
Any help is appreciated?