131–140 of 146 posts
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…
Earlier quoted context omitted.
We'll probably learn how to exploit human psychology faster than we learn how to treat or understand it.
I'm pretty sure we already have.
it feels like there's a new deep learning paper each week, ever so slightly bringing me closer to an existential nervous breakdown.
Yeah, I feel that. I tell myself they're only tools, really no stranger than time frequency domain transformations to someone unfamiliar with the fourier transform. Yet there really is something disturbing about seeing a computer resurrect so much of the mind of an artist who's been dead for nearly a century. I know the GPU doesn't understand what it's doing, but did Monet? When I paint I don't really understand. The…
Very little, arguably none. Imitation is far removed from creation. If you see an art student reproduce Monet's paintings or redo an existing image in Monet's style, would you ask "how much of Monet's mind is in the art student?"
At this point, I'm wondering "What's real any more..." Seriously, if this continues, I don't know how to keep up with this field. I spend at least an hour a day just reading about the work that has been done (i.e. reading the research).
I guess you already have read about Prisma App. Here is the research paper it is based on.. -> https://arxiv.org/pdf/1508.06576.pdf
Similar work DiscoGAN https://github.com/SKTBrain/DiscoGAN
/r/machinelearning asks how it's any different from DiscoGAN: https://www.reddit.com/r/MachineLearning/comments/62hzqc/r17... no answer so far.
>All DiscoGAN experiments are on 64x64, this is high resolution. I don't know whether this is an important difference though.
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…
It's 2017 and cars drive themselves. Isn't that exciting?
Really impressive stuff. Could this same technique be used on human photos to transfer traits like gender, age, ethnicity? Could be really useful for those age-progressed photos used in missing persons listings, for instance.
If you look at the nature of the transformations achieved in this paper, you might note that they are changes in, well, style. That is, the presentations of the objects in an image are represented using a different _style_, but they remain the same object.
As an example, take a look at the horse/zebra transformation; the horse obtains a zebra's stripes, but it _structurally_ still looks like a horse. That is to say, a zebra and a horse have identifiably different bodily proportions, and the horse's bodily proportions are not changed by the style transfer. Similarly, the trees in the summer -> winter transformation do not have the slightly saggy branches that they would due to the weight of snow on them.
With that in mind, I would be surprised if the approach taken in this work, taken as-is, would be able to change the gender or ethnicity of a person in a photo. There are structural differences between men's and women's faces, and similarly between races. I would imagine that an attempt of a race transfer, as I suppose you would call it, would largely amount to changing skin tone.
The age progression application, though, might work under the limitations I have speculated, at least for aging photos of a person that is already more or less mature. A person's facial structure does not significantly change once adulthood is reached, so simply transferring stylistic features might add wrinkles and other age-related changes in a realistic way.
Again, speculation. And even if the speculation is correct, that is not to say that some modifications to the approach would not be able to lift the constraints I made up.
Edit: reading through the rest of the paper[0] behind this work, looks like I might not be far off:
>Although our method can achieve compelling results in many cases, the results are far from uniformly positive. Several typical failure cases are shown in Figure 17. On translation tasks that involve color and texture changes, like many of those reported above, the method often succeeds. We have also explored tasks that require geometric changes, with little success.
The quote is from Section 6 ("Limitations and Discussion"), and example "limitations" are given in Figure 17.
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…
https://news.ycombinator.com/item?id=11452026
Really impressive stuff. Could this same technique be used on human photos to transfer traits like gender, age, ethnicity? Could be really useful for those age-progressed photos used in missing persons listings, for instance.
https://github.com/SKTBrain/DiscoGAN/blob/master/assets/m2f....
The full project can be seen here: https://github.com/SKTBrain/DiscoGAN
