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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 11
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      1/ Higher Top 1 accuracy in image classification actually isn't a great indicator that a new vision model is going to work out well in practice in an image to image task (like DeOldify). I've learned this the hard way after getting excited about a new shiny model many times!

      1 reply 5 retweets 36 likes
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    2. Jason Antic‏ @citnaj Jun 11
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      2/ EfficientNets come to mind- I just have not been seeing the benefits relative to the classification performance. But this really applies generally, even within a set of architectures that have been trained the same way (FaceBook's wsl models, BiTM, etc).

      1 reply 0 retweets 8 likes
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    3. Jason Antic‏ @citnaj Jun 11
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      3/ In those cases, you just don't really know until you actually try. You may be surprised, I can tell you that much! So don't just pick the one with the highest Top-1 accuracy in ImageNet...

      5 replies 1 retweet 11 likes
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    4. Sai Prasanna‏ @sai_prasanna Jun 11
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      Replying to @citnaj

      Sai Prasanna Retweeted Sai Prasanna

      I don't know whether this is tested for CV, but worth a try.https://twitter.com/sai_prasanna/status/1265730581586866182?s=19 …

      Sai Prasanna added,

      Sai Prasanna @sai_prasanna
      Replying to @dennybritz
      This paper has been in my partially read list. Statistically sound method for comparing model performace, might be relevant. https://www.aclweb.org/anthology/P19-1266/ … The authors had written a delightfully name "The Hitchhiker's guide to testing statistical significance in NLP" before this one.
      1 reply 0 retweets 1 like
      Jason Antic‏ @citnaj Jun 11
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      Replying to @sai_prasanna

      Sounds like a great lead: "Proper DNN comparison hence requires a comparison between their empirical score distributions on unseen data, rather than between single evaluation scores as is standard for more simple, convex models. "

      12:25 PM - 11 Jun 2020
      0 replies 0 retweets 0 likes

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