[HN Gopher] Pokemon GAN
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       Pokemon GAN
        
       Author : aliabd
       Score  : 58 points
       Date   : 2022-02-14 17:02 UTC (5 hours ago)
        
 (HTM) web link (huggingface.co)
 (TXT) w3m dump (huggingface.co)
        
       | The_rationalist wrote:
        
       | ulucs wrote:
       | Some examples look like they have picked two pokemon and tried to
       | merge them in a very weird way. I'll list what I see so others
       | can compare:
       | 
       | - 0 as Arceus + Shaymin S
       | 
       | - 1 as Lickilicky + Espurr
       | 
       | - 10 as Kirlia + Porygon 2, don't know where the colors are from
       | 
       | - 20 as Shellos + Froslass
       | 
       | - 30 as Roggenrolla/Oddish/Poliwag
       | 
       | - 42 as Garchomp + Trevenant
       | 
       | - I see the Kyurem in 60 but no idea where the tail is from
       | 
       | - 102 as Dragonite + Hitmonlee
        
         | minimaxir wrote:
         | The model appears to have overfit a bit (seed 535 is blatantly
         | Vivillion:
         | https://twitter.com/ak92501/status/1492560750103121920 )
         | 
         | Which is interesting because GANs tend not to overfit.
        
       | aliabd wrote:
       | Just rebuilt the app with more examples, so the queue should stay
       | a lot shorter now. This was built using Gradio[0] and is hosted
       | on Spaces[1]. Check out the paper[2] and github repo[3].
       | 
       | [0]: https://github.com/gradio-app/gradio [1]:
       | https://huggingface.co/spaces [2]:
       | http://www.cvlibs.net/publications/Sauer2021NEURIPS.pdf [3]:
       | https://github.com/autonomousvision/projected_gan
        
         | ghostly_s wrote:
         | The examples aren't displaying for me.
        
           | aliabd wrote:
           | They are the numbers at the bottom left of the interface
           | under the 'clear' button. Try clicking them and it will auto
           | fill the input then you just click submit. Or what do you
           | see?
        
       | aliabd wrote:
       | Just updated the link again - much faster now (inference down to
       | 0.5s from 9s) Queue will move a lot quicker now.
        
       | catillac wrote:
       | I am a big fan of hugging face, but whenever these examples come
       | up, they always seem to be half baked and don't quite work well,
       | and are not compelling. I think it would behoove them to polish
       | these a little more, in my opinion.
        
         | ianbutler wrote:
         | I believe this was made by a community member, huggingface
         | allows people to publish to their model repository to foster
         | more open sharing of work in the ML space. You should look at
         | this as in progress research and not a polished thing, and we
         | should be grateful the space is so open.
         | 
         | Edit: Double checking it looks like they are affiliated with HF
         | but it still is more of an open side project and anyone can
         | contribute to the repository so quality will vary.
        
         | minimaxir wrote:
         | These types of demos are typically intended for quick
         | interactive open-source proof-of-concepts rather than an e2e
         | app.
        
           | catillac wrote:
           | Ah that makes sense. Still, feels clunky and not compelling
           | imo, but I may not be the target audience.
        
             | themikesanto wrote:
             | Seems fine to me. It's a demo.
        
         | adamsmith143 wrote:
         | I think that's a feature of GANS more than anything else. Most
         | papers are cherry picking results to a pretty extreme degree so
         | that they look visually impressive but here you're seeing raw
         | results.
        
           | catillac wrote:
           | I am super familiar with the generative modeling space, have
           | designed them and deployed to real prod, and I will say
           | you're right that generative modeling results can be hit or
           | miss. But, I was more referring to the page layout, its
           | unreliability (seemed like the author was live debugging in
           | this thread?), poor UX (seems like people can't easily figure
           | out the page), etc. rather than anything about the model
           | itself (though tbh I also don't find Pokemon GAN Number
           | 10,000 compelling as a topic). Fundamentally though you do
           | have to always consider results from the latest papers with
           | some baseline skepticism.
        
       | minimaxir wrote:
       | For context, this is not the same technique that was used to
       | create the AI-generated Pokemon a couple months ago:
       | https://www.reddit.com/r/pokemon/comments/rgmyxp/i_trained_a...
       | 
       | That used a finetuned ruDALL-E which is not based on a GAN
       | architecture and is much, much slower than a GAN, albeit the
       | generated Pokemon are more coherent. You can play with it (with
       | no queue) on your own Colab Notebook here:
       | https://colab.research.google.com/drive/1A3t2gQofQGeXo5z1BAr...
        
         | isoprophlex wrote:
         | Very nice!
         | 
         | Unbelievable how clean looking and artifact-free these neural
         | net generated images have become (in a short short time!)
         | compared to eg. this deconvolved monstrosity
         | 
         | https://www.christies.com/media-library/images/features/arti...
        
       | arcticbull wrote:
       | These are wonderfully low-effort and I love each and every one of
       | them.
        
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       (page generated 2022-02-14 23:01 UTC)