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Are GAN's pretty hot these days or is that just a coincidence of the authors preferences? I just received Ian Goodfellow's Deep Learning textbook [1] and I know

by zengid 9y ago

Are GAN's pretty hot these days or is that just a coincidence of the authors preferences? I just received Ian Goodfellow's Deep Learning textbook [1] and I know he pretty much invented the technique, so I'm wonder how influential / important GANs are in the field. [1] http://www.deeplearningbook.org/ http://www.deeplearningbook.org/

habitue 9y ago
I don't think GANs are being used to do anything very serious at the moment (I would love to be corrected on this), but they're very exciting in terms of their capabilities and promise. Some good progress has been made over the last year in making them easier to train and making them so they produce varied outputs
- jamesblonde 9y ago
  WaveNet2 is using a variant of GANs to produce the most high fidelity speech (text->to->voice) of any computer system out there - in realtime. WaveNet from last year took 50 sec (i think) to generate 1 sec of audio. By using a variant of a GAN to re-engineer WaveNet, they can now produce real-time high quality text-to-voice: https://deepmind.com/documents/131/Distilling_WaveNet.pdf https://deepmind.com/documents/131/Distilling_WaveNet.pdf
  - argonaut 9y ago
    WaveNet2 is not a GAN, and describing it as a "variant of GANs" is like calling Python a variant of Java: it's highly misleading (WaveNet2 and GANs are both generative models, Python and Java are both programming languages). Also WaveNet2 makes no improvements in the actual quality of the model, only the run-time performance.
    - chillee 9y ago
      Yep, from the paper: > It is worth noting the parallels to Generative Adversarial Networks (GANs [7]), with the student playing the role of generator, and the teacher playing the role of discriminator. As opposed to GANs, however, the student is not attempting to fool the teacher in an adversarial manner; rather it cooperates by attempting to match the teacher’s probabilities. Furthermore the teacher is held constant, rather than being trained in tandem with the student, and both models yield tractable normalised distributions. WaveNet2's main resemblance to a GAN is that it uses another neural network for the loss function.
317070 9y ago
GANs are for graphics, not machine learning. They have no test scores as they cannot run on the test set. Therefore, its hard to tell how much they overfit. But they have uses, like for lossy compression of images or texture generation. Stuff which focuses on the graphical side of things where it outperforms machine learning methods with its crisper samples. For an overview of this argument: https://arxiv.org/abs/1511.01844 https://arxiv.org/abs/1511.01844 The idea of adversarial training is important and relevant in ML though! It allows for setting up losses which are hard to formulate otherwise.
- chillee 9y ago
  Huh? GANs are for graphics, not machine learning? I mean, first of all, how are GANs not machine learning? They are definitely "machine learning", and I don't think anyone would disagree. Second of all, GANs are most definitely not just for graphics. They've been applied to text generation, to generating adversarial examples, to data preprocessing, etc. Third, I have no idea what you even mean by "test set" in the context of GANs. It is true that it's hard to tell their performance, but that's irrespective of whatever you're talking about. It's hard to evaluate performance because we're usually judging the quality of the generated images, and we don't have any good ways of evaluating "perceptual loss", or how real an image looks. As for the OP, GANs have been a very hot topic. Not as hot as this blog post makes them look perhaps (with nearly every paper about them...), but I wouldn't really disagree with any of the papers posted. Only one I'm not familiar with is the "most useful" one, but the rest were all pretty great papers imo. As for Ian Goodfellow, he's a very smart guy who seems to do a pretty good job explaining things. I saw a couple YouTube videos from him at a meetup covering his DL book, and he did a great job teaching.
  - argonaut 9y ago
    Although I would agree that GANs are part of machine learning, some people definitely do disagree, and their concerns are valid. It's definitely an area of open research. Your third point is actually the point of those who disagree. It's the same reason why we have the principle of unfalsifiability in science.

by zengid 9y ago

habitue 9y ago

I don't think GANs are being used to do anything very serious at the moment (I would love to be corrected on this), but they're very exciting in terms of their capabilities and promise. Some good progress has been made over the last year in making them easier to train and making them so they produce varied outputs

jamesblonde 9y ago
WaveNet2 is using a variant of GANs to produce the most high fidelity speech (text->to->voice) of any computer system out there - in realtime. WaveNet from last year took 50 sec (i think) to generate 1 sec of audio. By using a variant of a GAN to re-engineer WaveNet, they can now produce real-time high quality text-to-voice: https://deepmind.com/documents/131/Distilling_WaveNet.pdf https://deepmind.com/documents/131/Distilling_WaveNet.pdf
- argonaut 9y ago
  WaveNet2 is not a GAN, and describing it as a "variant of GANs" is like calling Python a variant of Java: it's highly misleading (WaveNet2 and GANs are both generative models, Python and Java are both programming languages). Also WaveNet2 makes no improvements in the actual quality of the model, only the run-time performance.
  - chillee 9y ago
    Yep, from the paper: > It is worth noting the parallels to Generative Adversarial Networks (GANs [7]), with the student playing the role of generator, and the teacher playing the role of discriminator. As opposed to GANs, however, the student is not attempting to fool the teacher in an adversarial manner; rather it cooperates by attempting to match the teacher’s probabilities. Furthermore the teacher is held constant, rather than being trained in tandem with the student, and both models yield tractable normalised distributions. WaveNet2's main resemblance to a GAN is that it uses another neural network for the loss function.

317070 9y ago

GANs are for graphics, not machine learning. They have no test scores as they cannot run on the test set. Therefore, its hard to tell how much they overfit. But they have uses, like for lossy compression of images or texture generation. Stuff which focuses on the graphical side of things where it outperforms machine learning methods with its crisper samples. For an overview of this argument: https://arxiv.org/abs/1511.01844 https://arxiv.org/abs/1511.01844 The idea of adversarial training is important and relevant in ML though! It allows for setting up losses which are hard to formulate otherwise.

chillee 9y ago
Huh? GANs are for graphics, not machine learning? I mean, first of all, how are GANs not machine learning? They are definitely "machine learning", and I don't think anyone would disagree. Second of all, GANs are most definitely not just for graphics. They've been applied to text generation, to generating adversarial examples, to data preprocessing, etc. Third, I have no idea what you even mean by "test set" in the context of GANs. It is true that it's hard to tell their performance, but that's irrespective of whatever you're talking about. It's hard to evaluate performance because we're usually judging the quality of the generated images, and we don't have any good ways of evaluating "perceptual loss", or how real an image looks. As for the OP, GANs have been a very hot topic. Not as hot as this blog post makes them look perhaps (with nearly every paper about them...), but I wouldn't really disagree with any of the papers posted. Only one I'm not familiar with is the "most useful" one, but the rest were all pretty great papers imo. As for Ian Goodfellow, he's a very smart guy who seems to do a pretty good job explaining things. I saw a couple YouTube videos from him at a meetup covering his DL book, and he did a great job teaching.
- argonaut 9y ago
  Although I would agree that GANs are part of machine learning, some people definitely do disagree, and their concerns are valid. It's definitely an area of open research. Your third point is actually the point of those who disagree. It's the same reason why we have the principle of unfalsifiability in science.