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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

#121

Post by pjreddie »

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…

NIPS is more math/theory heavy as a conference while ICCV/CVPR/other vision conferences will tolerate less math and more pictures. You have probably been reading mostly vision papers but if you read more papers targeted at NIPS you will find more math. This looks to be an ICCV submission, whereas if you read the original GAN paper (a NIPS paper) there are theorems and proofs. Your observation is correct, you are just comparing different subsets of the field.

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

#122

Post by theossuary »

ooqr wrote: post #110
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.

I'm pretty sure we already have.

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

#123

Post by mrmaximus »

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…

I think this is because at it's core, the math behind deep learning is pretty simple and there's not much of it. Linear algebra, some simple activation functions and gradient descent. Implementing a net from scratch in python is pretty concise. The simplicity-to-effectiveness ratio is what I find so interesting about deep learning.

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

#124

Post by fudged71 »

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…

I wonder if all generated images should somehow be watermarked so that they can be determined some day to be false. Otherwise we could have a lot of false images around the web used for confirmation bias.

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

#126

Post by ganfortran »

kevinalexbrown wrote: post #118
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…

A friend of mine from academia was considering going into industry, so went to some data science meetups. Someone was giving a presentation about convolutional networks, yet did not know what a convolution was. At first I was startled, but in the long run machine learning applications will be decided just as much on user experience and design features as on algorithmic choices. I'm not a web programmer, but I imagine…

Math needs to fulfill a purpose in order to succeed in INDUSTRY. A image recognition is first and foremost about recognizing an image, the math behind it is mystical, but the accuracy is measurable.

If you want your knowledge to be hold as useful, then you need find a usage case for it. Simple as that. This is not only true for math, but for all the other techniques as well. Otherwise so-called knowledge is yet another self-indulgent toy, disconnected even further from being useful.

Contrary to what OP states here, recent development of WGAN and LSGAN pretty much math driven, and it leads to very useful realworld extension to the original model, that improves it quite a bit.

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

#127

Post by jszymborski »

kcorbitt wrote: post #99
habitue wrote: post #73

Earlier quoted context omitted.

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…

> The theory will eventually catch up Maybe... but the emergent behavior in a complicated system (and these networks are only getting more complicated, not less) is likely to quickly become more complex than the human mind can reasonably be expected to understand, given any amount of time. We actually know a lot less about biology, for example, than your typical "biology 101" course would lead you to believe. It's pr…

I mean, that's the nature of empirical sciences. You establish a model that fits observations you've made, but inevitably their is some chaotic, sometimes imperceptible noise that gets averaged out that represents some further level of complexity.

I wouldn't argue that it means we don't know much, in the end you can argue we don't know anything because all empirical sciences are beholden to the belief of abstractions that can be attributed to some imperceptible abstraction until you (don't) reach the bottom of the stack of turtles.

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

#128

Post by MattRix »

jordache wrote: post #27

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

Yes but I think you're missing the point that the neural network didn't need to be reprogrammed to specifically "winterize" the photos like your script did. It's much more general in its applications, and does a decent job no matter the lighting conditions etc.

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

#129

Post by HappyTypist »

anchpop wrote: post #66
thadjo wrote: post #62

Earlier quoted context omitted.

verifying the source of an image via a blockchain is probably going to have to be a thing

When all you have is a hammer, everything looks like a nail. What do blockchains have to do with this?

Blockchain are timestamped, cryptographically stamped and immutable.

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

#130

Post by [deleted] »

kcorbitt wrote: post #99
habitue wrote: post #73

Earlier quoted context omitted.

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…

> The theory will eventually catch up Maybe... but the emergent behavior in a complicated system (and these networks are only getting more complicated, not less) is likely to quickly become more complex than the human mind can reasonably be expected to understand, given any amount of time. We actually know a lot less about biology, for example, than your typical "biology 101" course would lead you to believe. It's pr…

[deleted]
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