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The relativistic discriminator: a key element missing from standard GAN

ajolicoeur.wordpress.com

21–30 of 33 posts

Re: The relativistic discriminator: a key element missing from standard GAN

#21
Interesting, especially the performance on bigger images, and it looks like a low-effort modification of many standard GAN losses. Seriously, I want to give this a try right now. What do GAN researchers think about this paper?

It should also be appreciated that it comes with code and a short blog post.

Re: The relativistic discriminator: a key element missing from standard GAN

#22
post #2

This is just confusing misuse of the word "relativistic", being based as it is on _relative_ probability and having little to do with philosophical relativism and nothing to do with physics relativity.

It is not quite relative but regularised by another GAN...

It's from same GAN though, C(x_r) and C(x_f) come from the same neural network. It's how realistic real data is compared to fake data (and vice-versa) as determined by C (discriminator without activation function).

Re: The relativistic discriminator: a key element missing from standard GAN

#23

Interesting, especially the performance on bigger images, and it looks like a low-effort modification of many standard GAN losses. Seriously, I want to give this a try right now. What do GAN researchers think about this paper? It should also be appreciated that it comes with code and a short blog post.

And a 24-page paper ;)

Re: The relativistic discriminator: a key element missing from standard GAN

#25

Same guy who wrote this has another post called Deep learning with cats[1] which has a great open source repo[2] with multiple GANs. 1. https://ajolicoeur.wordpress.com/cats/ 2. https://github.com/AlexiaJM/Deep-learning-with-cats

Same guy? Try... Not a guy!

Re: The relativistic discriminator: a key element missing from standard GAN

#26

what 'computer scientist' uses wordpress for their blog. Though, I don't have a blog, so...

I don't see any sense in attacking the credibility by the choice of the blogging infrastructure - the author wanted to convey a message, present his findings and he achieved that. This should be the only thing that counts. I guess the best (even if in a totally opposite field) argument against opinionated choice of web-technologies would be the website of Berkshire Hathaway [1]. They invest in highly sophisticated co…

It's not a him, right?

Re: The relativistic discriminator: a key element missing from standard GAN

#29
post #2

This is just confusing misuse of the word "relativistic", being based as it is on _relative_ probability and having little to do with philosophical relativism and nothing to do with physics relativity.

Many of the words used by the machine learning community are gratuitously unrelated to what they mean in other fields. If you hear a ML person use a term and you don't know the ML meaning, you probably need to ask for clarification.

This is true of every field of science and even social science I’ve known. Also the same terms have different meaning in different fields. Also the standard use of a term within a subfield can be different from its usage in another subfield. There are even variations in usage within communities of the same subfield (e.g. inside different ML communities).

Re: The relativistic discriminator: a key element missing from standard GAN

#30

Earlier quoted context omitted.

It's not even probability in standard GAN is it? Since you are taking the difference before the sigmoid. It can't really be interpreted as probability until it the range is clamped. Critic's difference or Critical Difference would be a better term perhaps.

It's logits or log odds or the logarithm of the odds, i.e. log(odds(p)) = log(p / (1 - p)): https://en.wikipedia.org/wiki/Logit Since sigmoid(C(x_r)) = p(x_r is real) and C(x_f) = p(x_f is real), the sigmoid of the difference expresses some probability that that x_r looks more real than x_f or vice versa (depending on whether it is C(x_r) - C(x_f) or C(x_f) - C(x_r)). Not sure whether there is a probabilistic interpr…

Correction: sigmoid(C(x_f)) = p(x_f is real)
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