A Style-Based Generator Architecture for Generative Adversarial Networks

What people said

Veedrac · 2019-08 · Reference

“Not all that difficult” is in the context of the brain, where things tend to vary between ‘pretty difficult’ and ‘seemingly impossible’. I say ML shows pattern matching of this sort isn't all that difficult because progress has been significant over very short stretches of time, without any particular need to solve hard problems, and with a general approach that looks like it will extend into the future. We have this famous image showing progress over the last 5 years. [LINK] The latest generator in this list has very powerful latent spaces, including approximately accurate 3D rotations. [VIDEO] We have similarly impressive image segmentation and pose estimation results. [LINK] Because you mentioned it, note that models that utilize multimodal perception is possible. The following uses audio with video. [LINK] For sure, these are not showing off the full breadth of versatility that humans have. I can still reliably distinguish StyleGAN faces from real faces, and segmentation still has issues. These all have fairly prominent failure cases, can't refine their estimates with further analysis like humans can, and humans still learn much, much faster than these models. However, note that (for example) StyleGAN has 26 million parameters, and with my standard approximate comparison of 1 bit:1 synapse, that puts it probably somewhere around the size of a honey bee brain. Given such a model is already capturing sophisticated models fairly reliably using sophisticated variants of old

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Veedrac · 2019-07 · Reference

> What exactly is incredible (relatively) about the current state of things? Compared to 1999? Watch [VIDEO] Hear [LINK] Read [LINK] These are not just ‘increasing numbers’. These are fucking witchcraft, and if we didn't live in a world with 5 inch blocks of magical silicon that talk to us and giant tubes of aluminium that fly in the sky the average person would still have the sense to recognize it. > It is the way to save this field from [...] For us to have a productive conversation here you need to either respond to my criticisms of this line of argument or accept that it's wrong. Being disingenuous because you like what the argument would encourage if it were true doesn't help when your argument isn't true. > Is rote multiplication a task that we're interested in achieving with AI? It's a measure for which improvement would have meaningful positive impact on our ability to reason, so it's a measure we should wish to improve all else equal. Yes, it's marginal, yes, it's silly, that's the point: failure in one corner does not equate to failure in them all.

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Liquix · 2019-04 · Reference

Do you believe this kind of thing should be/is detectable if you're looking for it hard enough? This is pretty realistic: [VIDEO]

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