Recurrent neural networks, dreams, and filling in

Just to note: although the models discussed in this blog can be arbitrary deep and rely on multilayer perceptrons, the principles of learning are completely different then in other contemporary deep learning: the error is injected at every level in a distributed way. There is no vanishing gradient, because there is no necessity to propagate anything end to end.

very interesting work...

so i was wondering, as far as "fill-in".

I see this is mainly focused on image synthesis for information that literally isn't available.

OTOH, occluders happen a lot in the real world just temporarily as things move around in front of us, or we move past an occluder.

In these cases we may have seen what is actually there just moments before the occluder appeared.

So it might be interesting to create a version that fills-in from what it had previously just seen rather from a corpus.

This way, if I'm shooting a video and there is an annoying lamp post that suddenly gets in the way it can be instantly erased. This would seem really useful for autonomous vehicle work as well.

I liked the ideas on sleep at the end as well w.r.t. attractors. I've thought quite a bit about "stuck target vectors" in the context of boid simulations. Really might be onto something there. Could have a loong chat about that.

Anyway, it's really cool, and great blog!