Wasserstein GAN · HackerPapers discussions

Wasserstein GAN — mentioned by tasdfqwer0897

Wed, 10 Apr 2019 02:58:06 GMT

Hmm, I'm not sure what you mean by applicable loss functions? I'll answer what I think you're asking and you can tell me if I got it wrong: There's been a lot of effort spent on coming up with different loss functions for GANs, but https://arxiv.org/abs/1711.10337 shows that, according to the metrics in problem 5, they don't really improve results compared to the original GAN loss function. There's something called a Wasserstein GAN https://arxiv.org/abs/1701.07875 that you may have heard about, but IMO the useful thing to take away from that paper is their 'gradient penalty' technique and…

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Wasserstein GAN — mentioned by phonebucket

Mon, 03 Dec 2018 18:48:04 GMT

Quote: “One of our assigned projects was to implement and train a Wasserstein GAN in TensorFlow. At this point, I had taken only an introductory statistics class that was a required general elective, and then promptly forgotten most of it. Needless to say, my statistical skills were not very strong. Yet, I was able to read and understand a paper on a state-of-the-art generative machine learning model...Yet, if you had asked me, or most of the students in that class, how to calculate the variance of a population, or to define marginal probability, you likely would have gotten blank stares. ” I…

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Wasserstein GAN — mentioned by SteveJS

Sun, 29 Apr 2018 21:35:40 GMT

There is a paper that indicates Earth mover, aka Wasserstein-1 is better for convergence of GANs: https://arxiv.org/pdf/1701.07875.pdf I tried it out on one of the Udacity Deep learning assignments using the Wasserstein loss functions built into tensorflow. I was unsuccessful in my limited use. The discriminator always ‘won out’ rather than the combo finding a saddle point. I eventually got my project to work without it, and did not go back to compare against just swapping EM back in.

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Wasserstein GAN — mentioned by backpropaganda

Wed, 02 Aug 2017 10:29:46 GMT

The kind of math that would be necessary to understand papers like [1][2][3][4]. [1]: https://arxiv.org/abs/1703.04933 [2]: https://arxiv.org/abs/1701.07875 [3]: https://arxiv.org/abs/1706.01350 [4]: https://arxiv.org/abs/1512.04860

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Wasserstein GAN — mentioned by jph00

Sat, 06 May 2017 22:04:18 GMT

It's not so much that there's just one or two specific results, but rather that there's just far more researchers working on this now, and quite a few are working on more theoretical stuff. Sometimes, that theory results in really practical outcomes - a good example would be https://arxiv.org/abs/1701.07875 . Or another by the same researcher: https://arxiv.org/abs/1506.00059 . We've seen, in the last year or two, interesting results in nearly every area of theoretical research of deep learning, including generalization, optimization, generative modelling, bayesian models, and network…

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Wasserstein GAN — HN submission

Thu, 09 Feb 2017 20:52:52 GMT

Wasserstein GAN

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Wasserstein GAN — HN submission

Mon, 30 Jan 2017 15:46:49 GMT

Wasserstein GAN

3 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.