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[D] Autoencoders vs VAE for semi-supervised learning

Hello,

I hope to clear a few doubts regarding Variational Autoencoders.

https://papers.nips.cc/paper/5352-semi-supervised-learning-with-deep-generative-models.pdf

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?

submitted by /u/question_hour
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.