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citnaj's profile
Jason Antic
Jason Antic
Jason Antic
@citnaj

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Jason Antic

@citnaj

Obsessively pursuing the perfection of image and video colorization/restoration using deep learning. Creator of DeOldify.

Ocean Beach, San Diego
deoldify.ai
Joined January 2010

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    1. Jason Antic‏ @citnaj Jun 16
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      Jason Antic Retweeted Jason Antic

      Reducing the impact of human bias and potential for error is key to getting an image model to actually work practically. When you're reduced to manual inspection, you have to be really, really skeptical of your perceptions and the way you're going about it. 1/https://twitter.com/citnaj/status/1273008694792339457 …

      Jason Antic added,

      Jason Antic @citnaj
      Replying to @cyrilzakka @jeremyphoward and 2 others
      That's what we had to do for NoGAN- find a stopping point based purely on visual inspection. And it was very fine grained- within 100-200 iterations- where you had to look! That was the attempt to "control" it. Makes it extremely hard to make repeatable experiments.
      1 reply 1 retweet 11 likes
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    2. Jason Antic‏ @citnaj Jun 16
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      Seriously, be equally skeptical when you're feeling great during evaluation as when you are feeling pessimistic. Keep as many things constant as possible. Do side by side comparisons. Sleep on it. Get others to help, and listen when they say you're out of your mind. 2/

      1 reply 0 retweets 4 likes
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    3. Jason Antic‏ @citnaj Jun 16
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      Separate benchmarks such as FID (Frechet Inception Distance) are great for sanity checking but are definitely not a replacement for visual inspection. Notably I haven't found a benchmark that suitably weighs in the negative impact of glitches. 3/

      1 reply 0 retweets 3 likes
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    4. Jason Antic‏ @citnaj Jun 16
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      Back to GANs: Because of the extra manual labor involved noted above, they severely reduce your ability to iterate, honestly compare experiments, and narrow down cause/effect. e.g. Is it really an improvement or just that you picked a particularly good stopping point? 4/

      1 reply 0 retweets 2 likes
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    5. Jason Antic‏ @citnaj Jun 16
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      So really a big part of getting a good model up is setting up the conditions to be able to iterate through the vast knob tuning landscape, quickly and accurately. Choice of approach (GAN versus not using a GAN) impacts this greatly! 5/

      1 reply 0 retweets 4 likes
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    6. Marcos Pereira‏ @voxelbased Jun 16
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      Replying to @citnaj

      GAN vs not using a GAN is too ambiguous

      1 reply 0 retweets 0 likes
      Jason Antic‏ @citnaj Jun 16
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      Replying to @voxelbased

      GANs have some unique instability issues compared to any other option I can think of. That's what I'm trying to point out here.

      4:26 PM - 16 Jun 2020
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      • Marcos Pereira
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