Comment by TTPrograms | Hacker News Reader

Comment by TTPrograms | Hacker News Reader

I'm surprised people are so blown away by this. It's a cool demonstration, but for this problem you have basically infinite training data. If you can find a latent space of faces this is hardly a stretch, since you already have a fantastic notion of locality in your data (by perturbing the camera). The interesting thing is generalization, which they show in figure 3B and is... ok, I guess. It's not that surprising compared to any of the other VAE stuff people have done (see the morphing scenes, 3D face illumination / rendering and furniture stuff from 2 years ago, for instance). It's also not that surprising compared to ex. the generative scene model RL paper that came out a few months ago (with Doom and the driving game). IMO deep learning research has moved beyond "here's another set of points I can fit a curve to". It really feels like this publication was heavily driven by prestige when most of the innovative stuff was achieved by other groups 2 years ago or more.

Ex. how's this different from https://arxiv.org/pdf/1503.03167.pdf from 2015?

I can't disagree with the contents of your comment; I upvoted it.

Yes, this work is incremental, not a breakthrough; it's a natural progression from a lot of other prior work.

BUT they're doing this with environments (not just with objects), with an agent that explores those environments, with color (unlike some of the older work), and with evident applicability in a range of "Open AI Gym"-style deep RL tasks.

I find the examples the authors show amazing. They feel qualitatively different. The fact that the work is incremental doesn't make it any less impressive.

BTW, I remember reading that paper from MIT when it came out; I find it also amazing -- even if in the end it's all only "fitting a manifold to a set of points."

I don't think the breakthrough here is generating a 3D space from 2D snapshots.

I think it's the idea that a network capable of doing that is a far, far better input to training an agent than flat images, or even the ground-truth 3D space.

They address that paper specifically here:

"GQN uses analysis-by-synthesis to perform “inverse graphics,” but unlike existing methods (42), which require problem-specific engineering in the design of their generators, GQN learns this behavior by itself and in a generally applicable manner. However, the resulting representations are no longer directly interpretable by humans."

This is not completely true - they provide labelled poses as part of the training set. The differences are really minor.

Maybe it's just me, but I had no idea any of this was currently possible. Any cool videos about this we should see for further mind-blowing?

To put this into a larger context: I wonder, do we currently have the technology to see another major breakthrough in AI, because frankly all I’m seeing are impressive, but nonetheless incremental advancement.

Don't worry op I'm sure deepmind patented this approach.

They're trying to, actually, the Science paper gives the patent application number.

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