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Generative Adversarial Networks – The Story So Far

blog.floydhub.com

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Re: Generative Adversarial Networks – The Story So Far

#21

Post by mellosouls »

Thank you for this useful summary; I think you should consider a discussion of prior work which may undermine the "revolutionary idea over a pint of beer" narrative, but might encourage independent researchers to press on beyond the idea phase.

Wikipedia GAN history (and talk) indicates it's not quite as clear cut as your framing, and this answer below on that subject from somebody who blogged the idea several years previously demonstrates there is often a hinterland of discussion that gives rise to the (genuinely) independent ideas of Goodfellow et al.

In this case the central idea doesn't seem to have been completely original to Goodfellow, though the credit is his for fully pursuing it to implementation in the current model.

https://stats.stackexchange.com/a/301280

Note: the linked answer - while in the context of the well known Schmidhuber can of worms, is actually the more obscure and very polite challenge from Niemitalo (who is mentioned in the Wikipedia history). The point stands though, regardless of actor.

Re: Generative Adversarial Networks – The Story So Far

#22

Post by ReDeiPirati »

gwern wrote: post #11
heydenberk wrote: post #8

Earlier quoted context omitted.

I put your question in talktotransformer.com and got this response: I know people have been having trouble adapting these kinds of generative techniques to text. Do you know of anyone making interesting progress there? It is difficult to make progress in the field of generative methods with text alone - it takes effort and creativity to get a generative system working. A big part of our research focuses on generating…

GPT-2 is, however, not adversarial at all, and that might be part of why it rambles and lacks consistency or much of a 'point'.

+1. Also, the author of this article wrote a terrific article on GPT-2 as well. I'll definitively recommend you to check it out if you are interested in the latest breakthrough of text generation: https://blog.floydhub.com/gpt2/

Re: Generative Adversarial Networks – The Story So Far

#23

Post by hjk05 »

I feel like the reporting on these things using just the generated images is completely off point. Showing an image of a face and saying: “can you believe this is fake isn’t it amazing?” Just has me going meh? At the very minimum show the “completely fake” generated image next to the closest image from the training sets used. Otherwise how do I know you haven’t just built an over-engineered solution that picks a random number between 0 and the number of images you had available for training?

Re: Generative Adversarial Networks – The Story So Far

#24

Post by galaxyLogic »

Erlich_Bachman wrote: post #20
galaxyLogic wrote: post #9

This basically means you can take any politician or citizen and create a video in which they say or do bad (but fake) things. Age of Fake News has truly arrived. Is there some research on how to distinguish fake generated videos from real ones?

You could produce very good photoshop fakes like these for a very long time now. It has had basically no effect on the politics and everyone knows that this technology exists and treats all the pictures they see accordingly (checking their sources etc). It will just be the same with video.

> everyone knows that this technology exists and treats all the pictures they see accordingly

I don't know if everybody knows that. Lot of people believe Hillary ran a child-sex operation and should be locked up. They chant it. Trump has lied 10,000 times and still a lot of people believe him. Surely they would believe a fake video as well, many of them.

Re: Generative Adversarial Networks – The Story So Far

#25

Post by astazangasta »

hjk05 wrote: post #23

I feel like the reporting on these things using just the generated images is completely off point. Showing an image of a face and saying: “can you believe this is fake isn’t it amazing?” Just has me going meh? At the very minimum show the “completely fake” generated image next to the closest image from the training sets used. Otherwise how do I know you haven’t just built an over-engineered solution that picks a rand…

The nature of the Generator is that it is seeded with random inputs and trains so it is able to fool the adversarial classifier. I.e. it never sees the "true" data.

Re: Generative Adversarial Networks – The Story So Far

#26

Post by rytill »

astazangasta wrote: post #25
hjk05 wrote: post #23

I feel like the reporting on these things using just the generated images is completely off point. Showing an image of a face and saying: “can you believe this is fake isn’t it amazing?” Just has me going meh? At the very minimum show the “completely fake” generated image next to the closest image from the training sets used. Otherwise how do I know you haven’t just built an over-engineered solution that picks a rand…

The nature of the Generator is that it is seeded with random inputs and trains so it is able to fool the adversarial classifier. I.e. it never sees the "true" data.

This isn’t completely accurate. The generator sees the training data in the same way supervised learning might, because the discriminator sees the data, and the generator shares gradients with the discriminator.

Your point stands though, that it’s obviously not overfitting, and no scientist would publish a result that was just overfitting face generation.

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