Also when you use a dataset containing watermarked stock photos and the watermark is treated as a signal instead of noise :) https://i.imgur.com/O1ZRwSF.jpg
But now, in 2019, I've fully acclimated such nightmare-fuel concoctions: "Oh look, another GAN cat!"
There are three of these generators that have shown up on HN in the last few days—people, cats, and anime faces—and what the other two (more successful) ones have in common is that the things they're trying to generate all have the same basic shape and structure: that of a face.
The cat images in the training data are clearly just cats from every angle. There's much less of a clear structure for the neural net to recognize and reproduce.
That it's doing as well as it is is actually kind of remarkable, given that issue.
If it's not, that should be the definition for machine learning.
> At least one word was found in each of the six source languages (Chinese, English, Hindi, Spanish, Russian, Arabic) corresponding to the proposed gismu. This word was rendered into Lojban phonetics rather liberally: consonant clusters consisting of a stop and the corresponding fricative were simplified to just the fricative (“tc” became “c”, “dj” became “j”) and non-Lojban vowels were mapped onto Lojban ones. Furthermore, morphological endings were dropped. The same mapping rules were applied to all six languages for the sake of consistency.
Wait, what’s a cat again?
At the very center of the hole, everything cancels. But it is unstable. As you head in any direction, you will get pulled further. Which means that there are also tides pulling you apart.
At the center of the solid ring of the torus, everything also cancels. But this time the tides are squishing you together. Which is why the configuration is stable.
Another thing that really bothers me is that no one tries to replicate any of these results without neural networks. To most people here this is the natural result of deep neural networks being the bestest algorithm ever. To me, this indicates that much of the current ML research fails to generate true insight.
 For example, what would GAN-like architecture look with gcForests? No one seems to care about questions like this, even though gcForests have tons of practical advantages over neural nets.
I don't think anyone who knows what they're talking about would say otherwise.
Even in this difficult comparison you can see the non-human repeating skin patterns on the right and the awkward teeth contour. Also hair-on-skin often looks wet and with unnatural bends.
When comparing wrinkly people then it gets a little harder.
Look at the clothes and necklace. The clothes are different on left and right sides of her face - the moment you see it you can't unsee it and it's obviously wrong.
I think we've seen what these latest gen GANs can do with natural images, so why not try a novel application ?
They can do something to imitate text in images, so there's reason to think ppt slides should work.
and these recordings dont really exist
Disclaimer: Fun useless side project by me
(And contains no machine learning, but just plain Markov chains)
Domain Name: THISWEBSITEDOESNOTEXIST.COM
Creation Date: 2005-08-26-T19:04:47Z
Updated Date: 2018-08-23-T01:12:14Z
Registrar Registration Expiration Date: 2019-08-26
Raises some questions about what is able to be copyrighted vs. derived works if the generated image was produced by this algorithm and doesn't actually exist in Shutterstock's (excuse me, Shuttersrstsck's) database.
Common cuckoos lay their eggs in other birds' nests. The chicks don't necessarily look much like the host species to the human eye, but they can fool their hosts along the correct dimensions to get food from them. It's an interesting question to what degree ML algorithms trained on human dimensions could be foiled by an animal whose brain has been wired for different perceptions, or how feasible it is to train an ML algorithm on animal perception, or if it's possible to make an algorithm that successfully fools, say, both man and dog.
On the last point, for example: to make fake sounds that fool animals with different hearing ranges, presumably you have to be able to output sounds across the union of the ranges and train on sound data over the union of the ranges.
(Note: I'm not a biologist, if someone more informed wants to correct me on anything here you are welcome to do so.)
One looks like a cat, the other...?
This is a cat version, but it works out a lot worse. Some are fine, some are terrible.
for some reason shutterstock showed up on the bottom of the preview image when i posted it to my discord channel
for some reason shutterstock showed up at the bottom of the discord image preview XD