Note on 16:38. The classifier doesn't directly classify the ground-truth factor corresponding to each latent variable; it classifies the factor that was kept constant in each input data pair. However, the structure of the problem, and the limitation of a linear classifier, ensures that it can only do this by mapping latent variables to ground-truth factors.
I appreciate the comment! Yeah, the theory of VAEs can get a bit heavy at times... though I hope some points are conveyed well enough without the need for equations
Absolutely amazing video! Honestly, perfect explanation!
Note on 16:38. The classifier doesn't directly classify the ground-truth factor corresponding to each latent variable; it classifies the factor that was kept constant in each input data pair. However, the structure of the problem, and the limitation of a linear classifier, ensures that it can only do this by mapping latent variables to ground-truth factors.
But this means we need to know the concepts that creates the chair before hand
Which in return means we may overlook some important concepts
Very interesting indeed, I feel kinda stupid cuz I barely understand the math tho lol.
I appreciate the comment! Yeah, the theory of VAEs can get a bit heavy at times... though I hope some points are conveyed well enough without the need for equations