and 1:01:50 => With the joint embedding architecture- What would be inference with this architecture, inferring a Y from a given X minimizing the cost C(h, h')? I know that you could run gradient descent to the Y backward the Pred(y) network but it is not clear to me the purpose of inferring Y given X in this architecure.- What does the "Advange: no pixel-level reconstruction" in green means? (I suspect that this may have something to do with my just above question)- Can this architecture also be trained as a Latent Variable EBM? or it is always trained in a Contrastive way(00:25:10 - 01:51:31)
05L – Joint embedding method and latent variable energy based models (LV-EBMs)
Alfredo Canziani
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