51–60 of 146 posts
I wonder if there is a way to fix this, possibly by stacking another GAN on top?
I've only recently started reading about deep neural networks, and the thing that strikes me the most about the literature is the lack of mathematics . Open a NIPS paper from 2010 or so, and you'll see extremely dense mathematics: nonparametrics, variational approximation, sampling theory, riemannian geometry. But from my (admittedly small) sampling of the convnet / RNN literature there really doesn't seem to be much…
We don't have much math I'm aware of that can describe the capabilities of different network configurations from first principles. Even though we constructed the network it feels like we're back to the beginnings of science with this one.
Let's change this and see what happens.
Earlier quoted context omitted.
So... photoshop?
I think beyond photoshop - more like the ability to seamlessly insert or "imagine" situations that never occurred in real life. For instance, imagine if you could feed in a ton of images of person A - then a ton of images of porn - then have the system imagine a porn scene. ...or a drug-usage scene. ...or any other thing you could think up. It's even possible this could be pushed into a generated movie. You could eve…
I've only recently started reading about deep neural networks, and the thing that strikes me the most about the literature is the lack of mathematics . Open a NIPS paper from 2010 or so, and you'll see extremely dense mathematics: nonparametrics, variational approximation, sampling theory, riemannian geometry. But from my (admittedly small) sampling of the convnet / RNN literature there really doesn't seem to be much…
I think once the underpinnings were created via math it became questions about what network geometries to use. We don't have much math I'm aware of that can describe the capabilities of different network configurations from first principles. Even though we constructed the network it feels like we're back to the beginnings of science with this one. Let's change this and see what happens.
While this is undoubtedly very impressive, I think it's just another logical step to what we've been seeing so far: - In the past, you needed to have a pianist at home to perform you a song, with the music box and then the phonograph you don't need to hire anyone anymore. It's probably not as good as a live performance (maybe?), but it's good enough for many people, and much much cheaper, faster, and available. - You…
While this is undoubtedly very impressive, I think it's just another logical step to what we've been seeing so far: - In the past, you needed to have a pianist at home to perform you a song, with the music box and then the phonograph you don't need to hire anyone anymore. It's probably not as good as a live performance (maybe?), but it's good enough for many people, and much much cheaper, faster, and available. - You…
The next step would be to have it actually turned into an actual canvas rendering, with the texture and such of actual paint. Could probably be done using 3d printer technology, inkjet, and/or robotics in some manner... ...in fact, I know you can already get photos printed to canvas - but taking it to the next stage of texture would be amazing - right now, I think the best you can get is to have a trained person "hig…
TIL - remap green pixels to grey/white and you go from summer to a contrived notion of winter. This done easily in Photoshop. - select green pixels, smooth it a bit, then paint white over it.. - apply a blue cast on it https://s17.postimg.org/q68dz04sf/test.jpg
I'm still up in the air on how much of this is an amazing breakthrough in cognitive image manipulation and how much is a parlor trick. It is very cool, but the scope seems a little too narrow in their examples.
TIL - remap green pixels to grey/white and you go from summer to a contrived notion of winter. This done easily in Photoshop. - select green pixels, smooth it a bit, then paint white over it.. - apply a blue cast on it https://s17.postimg.org/q68dz04sf/test.jpg
It's a lot cheaper to get an ai to do it than a person.
Earlier quoted context omitted.
I think once the underpinnings were created via math it became questions about what network geometries to use. We don't have much math I'm aware of that can describe the capabilities of different network configurations from first principles. Even though we constructed the network it feels like we're back to the beginnings of science with this one. Let's change this and see what happens.
The thing is, the underpinnings have been there for decades. The convnet was invented in the '90s. The only thing that's changed is the availability of data and processing power.