Description
What are joint (rotationally-invariant) variational autoencoders, and why would we need them? The short answer to the first question is that j(r)VAE is a version of a variational autoencoder where one ...
Summary
- What are joint (rotationally-invariant) variational autoencoders, and why would we need them?
- Hence, we would like to use unsupervised ML to find relevant traits within the data, and that’s exactly where variational autoencoders with their capability to disentangle data representations and find a priori unknown factors of variability within the data can come in handy.
- Images by the authors.
- When we increase the orientation disorder, a = 120, we start to see more than one cluster for each hand, e.g.
- there is a cluster corresponding to two orientations of clubs and spades rotated by 120 degrees.