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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

junyanz.github.io

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Re: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

#101

Post by felippee »

rmellow wrote: post #68
felippee wrote: post #54

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…

I understand your position, but it comes from a false assumption that this is just about images. This is a demo that uses images as a way to "wow" the audience. 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…

It is not entirely obvious that such extrapolations would succeed. GAN is not a new idea (new in the context of deep learning) but automatic generation of adversarial samples is a known and exploited subject (even e.g. in the field of evolutionary strategies). So this is still the promise: look, it works on the images but there are all these wonderful applications possible. Its 2017 now, I'll check back in 2019 to see if anything has changed.

Re: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

#102

Post by ddrager »

dharma1 wrote: post #15

Very cool. What's the max resolution (on a 12GB GPU)?

I'm curious too, since the max resolution appears to be 220x220 on a 2GB GPU in my testing. If that is a linear relationship, it seems like it would be ~ 1080x1080 for a 12GB GPU.

Re: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

#103

Post by scott_s »

polyfractal wrote: post #100
scott_s wrote: post #84

Earlier quoted context omitted.

Agreed with the first paragraph, but I think this sort of behavior is normal for most disciplines. That is, it's not CS looking like psychology or sociology, it's normal for CS to do this. I think we're seeing a similar thing right now in biology with CRISPR.

FWIW, CRISPR isn't a great analogy. It's a well understood mechanism, and not really all that different from standard genome editing techniques like restriction enzymes, zinc-finger nucleases, etc. It's new and flashy and super powerful -- and the applications of CRISPR are still being discovered -- but the mechanism is understood and simple.

That wasn't the aspect I was going for. More for a period of incremental improvements in understanding, from which emerges a particular thing with enormous potential, and then the field is dominated by applications of that new thing.

Re: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

#104

Post by WhiteNoiz3 »

soVeryTired wrote: post #50

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…

Neural networks are very much math based (more so than I imagined when I first started reading about them), it's just the underlying math hasn't changed much - only the architectures and techniques.

Re: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

#105

Post by [deleted] »

eriknstr wrote: post #76

Current title: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Current link: https://junyanz.github.io/CycleGAN/ Previous title: Berkeley's software turns paintings into photos, horses into zebras, and more Previous link: https://github.com/junyanz/CycleGAN/ The previous title, which was based on the repo description of the previous link, was much more informative to me.

[deleted]

Re: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

#106

Post by koala_man »

ddrager wrote: post #102
dharma1 wrote: post #15

Very cool. What's the max resolution (on a 12GB GPU)?

I'm curious too, since the max resolution appears to be 220x220 on a 2GB GPU in my testing. If that is a linear relationship, it seems like it would be ~ 1080x1080 for a 12GB GPU.

If it's linear with pixel count, then it would be ~500*500.

Re: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

#108

Post by VikingCoder »

habitue wrote: post #73
soVeryTired wrote: post #50

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…

So what we're seeing is these fields pre-deep learning were mathematical disciplines making steady progress on well-understood foundations. Then deep learning came in, was exceptionally effective at problems that had been difficult to crack, and people shifted focus because it seems weird to be diddling around with incremental gains on techniques that are significantly less effective. What this created though, was a…

It's data-driven... rather than theory-driven.

In some respects this is good, in other respects it's awful.

Re: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

#109

Post by waqf »

eriknstr wrote: post #76

Current title: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Current link: https://junyanz.github.io/CycleGAN/ Previous title: Berkeley's software turns paintings into photos, horses into zebras, and more Previous link: https://github.com/junyanz/CycleGAN/ The previous title, which was based on the repo description of the previous link, was much more informative to me.

I would be very interested to see software that turns horses into zebras, but I don't believe this is that, this is just software that manipulates images.

Re: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

#110

Post by ooqr »

MR4D wrote: post #87

My one takeaway from this is that the future will be a scary place. This is impressive work, so please don't take that as a knock against this phenomenal work. This shows that computers soon will have the ability to fool our senses so well that we may not even believe reality when it is right in front of us. Some of the pictures, when I was just viewing them (before reading captions or titles) looked real. I was asto…

We'll probably learn how to exploit human psychology faster than we learn how to treat or understand it.
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