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Hi everyone. I just published a new blog post which talks about the evolution of GANs over the last few years. You can check it out here. I think it's fascinat

by iyaja 7y ago

Hi everyone. I just published a new blog post which talks about the evolution of GANs over the last few years. You can check it out here. I think it's fascinating to see sample images generated from these models side by side. It really does give a sense of how fast this field has progressed. In just five years, we've gone from blurry, grayscale pixel arrays that vaguely resemble human faces to thispersondoesnotexist, which can easily fool most people on first glance. Apart from image samples, I've also included links to papers, code, and other learning resources for each model. So this article could be an excellent place to start if you're a beginner looking to catch up with the latest GAN research. I hope you enjoy it!

isoprophlex 7y ago
This was a very enjoyable read, thank you! You do a great job in making these concepts understandable. The self attention mechanisms caught my eyes. Going to look into implementing something like that for a toy dataset. Thanks for the inspiration
xvf22 7y ago
This is really helpful, I've been wanting to deep dive on GANs and this has pushed me to do it. I like the mix of images and explanations. Thanks!
6gvONxR4sf7o 7y ago
I know people have been having trouble adapting these kinds of generative techniques to text. Do you know of anyone making interesting progress there?
- heydenberk 7y ago
  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 sequences which correspond to handwritten data, and to improving on that method we have developed generative techniques which allow us to generate a large range of novel sequences. Our work is still small, but we are not stopping there, in fact our next major research project is to generate novel sequences for novel languages. What I see is that we are still in an early stage when it comes to the technology used in generative methods to create new words, but I suspect this is due to a combination of factors. First and foremost is the fact that the techniques we've developed to generate novel sequences are highly specialized in a particular kind of context - we are not going to create random numbers or sequences because that just doesn't work. Generating a word, for example, uses very specific computational principles and can only be done if you are aware of the context in which it is being generated (or "determined" as the linguists would say). Even so, the general principle has been around so long, that one could quite easily create several different methods to create.
  - gwern 7y ago
    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'.
    - ReDeiPirati 7y ago
      +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/ https://blog.floydhub.com/gpt2/
- lettergram 7y ago
  Yes! https://medium.com/capital-one-tech/why-you-dont-necessarily-need-data-for-data-science-48d7bf503074 https://medium.com/capital-one-tech/why-you-dont-necessarily... GANs simply try to replicate a set of features - you can think of this as images or text. Variations in the GAN designs will be present, but the general principles are the same.
amelius 7y ago
Cool! But one thing I'd like to see discussed is to what extent the images in various publications have been cherry-picked.
- tachyonbeam 7y ago
  I'm curious about that too, and I'd like to know if there's been much work on GANs that can generate videos.
- Scaevolus 7y ago
  Cherry picking is no longer necessary with recent advancements. The images on https://thispersondoesnotexist.com/ https://thispersondoesnotexist.com/ are random and have a few artifacts (particularly the backgrounds and hair), but if you weren't looking for it you'd be unlikely to notice anything.

by iyaja 7y ago

isoprophlex 7y ago

This was a very enjoyable read, thank you! You do a great job in making these concepts understandable. The self attention mechanisms caught my eyes. Going to look into implementing something like that for a toy dataset. Thanks for the inspiration

xvf22 7y ago

This is really helpful, I've been wanting to deep dive on GANs and this has pushed me to do it. I like the mix of images and explanations. Thanks!

6gvONxR4sf7o 7y ago

I know people have been having trouble adapting these kinds of generative techniques to text. Do you know of anyone making interesting progress there?

heydenberk 7y ago
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 sequences which correspond to handwritten data, and to improving on that method we have developed generative techniques which allow us to generate a large range of novel sequences. Our work is still small, but we are not stopping there, in fact our next major research project is to generate novel sequences for novel languages. What I see is that we are still in an early stage when it comes to the technology used in generative methods to create new words, but I suspect this is due to a combination of factors. First and foremost is the fact that the techniques we've developed to generate novel sequences are highly specialized in a particular kind of context - we are not going to create random numbers or sequences because that just doesn't work. Generating a word, for example, uses very specific computational principles and can only be done if you are aware of the context in which it is being generated (or "determined" as the linguists would say). Even so, the general principle has been around so long, that one could quite easily create several different methods to create.
- gwern 7y ago
  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'.
  - ReDeiPirati 7y ago
    +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/ https://blog.floydhub.com/gpt2/
lettergram 7y ago
Yes! https://medium.com/capital-one-tech/why-you-dont-necessarily-need-data-for-data-science-48d7bf503074 https://medium.com/capital-one-tech/why-you-dont-necessarily... GANs simply try to replicate a set of features - you can think of this as images or text. Variations in the GAN designs will be present, but the general principles are the same.

amelius 7y ago

Cool! But one thing I'd like to see discussed is to what extent the images in various publications have been cherry-picked.

tachyonbeam 7y ago
I'm curious about that too, and I'd like to know if there's been much work on GANs that can generate videos.
Scaevolus 7y ago
Cherry picking is no longer necessary with recent advancements. The images on https://thispersondoesnotexist.com/ https://thispersondoesnotexist.com/ are random and have a few artifacts (particularly the backgrounds and hair), but if you weren't looking for it you'd be unlikely to notice anything.