Skip to main content

Blog

Learn About Our Meetup

5000+ Members

MEETUPS

LEARN, CONNECT, SHARE

Join our meetup, learn, connect, share, and get to know your Toronto AI community. 

JOB POSTINGS

INDEED POSTINGS

Browse through the latest deep learning, ai, machine learning postings from Indeed for the GTA.

CONTACT

CONNECT WITH US

Are you looking to sponsor space, be a speaker, or volunteer, feel free to give us a shout.

[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
[link] [comments]