61–70 of 146 posts
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…
The next step is "turning pencil drawings into photos" and using it to fabricate evidence on grand scale. Why bother catching politicians doing something when you can just draw them in? Will wreak havoc on societies with weak politics/reporting culture.
This will hurt journalists reporting on facts more than it will hurt targets of smear campaigns. Journalist: "Here is a compromising photo of a politician." Politician: "Here are 1M photos of every politician doing every imaginable illegal act. Prove that your one photo is not similarly fabricated."
It wasn't highlighted on the github readme, but I think that the satellite photo to map and map to satellite photo (!!!) is incredible as well: https://taesung89.github.io/cyclegan/2017/03/25/maps-compari...
i don't even know how that's possible. How could it recover a golf course?
This has also been making the rounds, Deep Photo Style Transfer: https://github.com/luanfujun/deep-photo-styletransfer
I wonder if it could start with input of a normal portrait of you, style: some celebrity of your gender, and output: what you would look like after receiving the same style? It doesn't seem beyond the examples shown at your link...
It would bring a whole new meaning to the word "filter" (instagram, etc.) I particularly like that the original is very much present in the output: it would still be "you".
But maybe there are subtle problems that I don't notice because I don't look at the subject matter as carefully. People pay a lot of attention to recognizing each other, perhaps the effect would not transfer as well as these examples presented.
Earlier quoted context omitted.
This will hurt journalists reporting on facts more than it will hurt targets of smear campaigns. Journalist: "Here is a compromising photo of a politician." Politician: "Here are 1M photos of every politician doing every imaginable illegal act. Prove that your one photo is not similarly fabricated."
verifying the source of an image via a blockchain is probably going to have to be a thing
The next step is "turning pencil drawings into photos" and using it to fabricate evidence on grand scale. Why bother catching politicians doing something when you can just draw them in? Will wreak havoc on societies with weak politics/reporting culture.
errr.... I hate to break it to you but... https://github.com/phillipi/pix2pix/blob/master/imgs/example...
This style transfer idea, though eye candy and sometimes impressive, seems to be the core application of deep learning these days. François Chollet tweeted something like that two years ago about the prisma app (yes it was 2015). He back then anticipated many other killer apps behind the corner but it seems not much had materialized. It's 2017 now and people are still super excited about yet another style transfer ne…
However, the kind of network used here (GAN) can be used in domains other than images (text, financial data).
Imagine if you trained a network to generate fraudulent financial data and another to become an expert at catching fraud, each feeding back into the other's skill. This is the concept of GANs at heart and definitely disruptive if correctly executed.
Maybe I'm out of the loop, but I haven't seen anything demonstrating results on "data" – the kinds of challenges that are actually valuable to businesses.
Why is that? Are those just less sexy / more proprietary in nature, or is there something about those challenges that make NN's less useful to them?
