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] Why does Beta-VAE help in learning disentangled/independent latent representations?

In Beta-VAE paper (https://openreview.net/pdf?id=Sy2fzU9gl), the authors mentioned that having Beta > 1 helps the network in learning independent latent representations. However, in VAE, the posterior distribution itself is assumed to be a Gaussian with a diagonal covariance matrix, i.e.

q(z|x) = N(U(x),Cov(x)) where Cov(x) is a diagonal matrix.

This means that we are inherently generating latents that will be independent given an input image x. So why does increase learning pressure on the KL divergence term between posterior and Gaussian prior should help any more in learning independent latents when posterior is already assumed to be independent?

submitted by /u/shamitlal
[link] [comments]

Next Meetup

 

Days
:
Hours
:
Minutes
:
Seconds

 

Plug yourself into AI and don't miss a beat

 


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.