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I think the idea is the network is completely trained and encoded along with the image and delta data. A new network would just require retraining and storing t

I think the idea is the network is completely trained and encoded along with the image and delta data. A new network would just require retraining and storing that new network along with the image data. It doesn't use a global network for all compressions.

codeflo 7y ago
I don't think this would work, the size of the network would likely dominate the size of the compressed image.
- mstade 7y ago
  Wouldn't the network be part of the decoder?
  - mastre_ 7y ago
    Yes, and this is why you couldn't update the network. Still, much like how various compression algos have "levels," this standard could be more open in this regard, adding new networks (sort of what others above refer to as versions) and the image could just specify which network it uses. Maybe have a central repo from where the decoder could pull a network it doesn't have (i.e. I make a site and encode all 1k images on it using my own network, pull the network to your browser once so you can decode all 1k images). And even support a special mode where the image explicitly includes the network to be used for decoding it along with image data (could make sense for a very large images, as well as for specialized/demonstrational/test purposes). All in all, a very interesting idea.
    - mstade 7y ago
      I wonder what the security implications of all this is, sounds dangerous to just run any old network. I suppose maybe if it's sandboxed enough with very strongly defined inputs and outputs then the worst that could happen is you get garbled imagery?
stefs 7y ago
they include the trained models under the "model weights" section. imagenet is ~20mb, openimages is ~17mb. now this might be prohibitive for images over the web, but it'd be interesting whether it might be applicable for images with huge resolutions for printing, where single images are are hundreds of megabytes

codeflo 7y ago

I don't think this would work, the size of the network would likely dominate the size of the compressed image.

mstade 7y ago
Wouldn't the network be part of the decoder?
- mastre_ 7y ago
  Yes, and this is why you couldn't update the network. Still, much like how various compression algos have "levels," this standard could be more open in this regard, adding new networks (sort of what others above refer to as versions) and the image could just specify which network it uses. Maybe have a central repo from where the decoder could pull a network it doesn't have (i.e. I make a site and encode all 1k images on it using my own network, pull the network to your browser once so you can decode all 1k images). And even support a special mode where the image explicitly includes the network to be used for decoding it along with image data (could make sense for a very large images, as well as for specialized/demonstrational/test purposes). All in all, a very interesting idea.
  - mstade 7y ago
    I wonder what the security implications of all this is, sounds dangerous to just run any old network. I suppose maybe if it's sandboxed enough with very strongly defined inputs and outputs then the worst that could happen is you get garbled imagery?

stefs 7y ago

they include the trained models under the "model weights" section. imagenet is ~20mb, openimages is ~17mb. now this might be prohibitive for images over the web, but it'd be interesting whether it might be applicable for images with huge resolutions for printing, where single images are are hundreds of megabytes