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I have a suspicion that in the future computers will use a vaguely DNN-like approach for compression, but I haven't seen any super compelling examples of this y

I have a suspicion that in the future computers will use a vaguely DNN-like approach for compression, but I haven't seen any super compelling examples of this yet. I can say "imagine a photo taken at 2000 feet and 2 miles away of the Eifel Tower with a beautiful orange sunset in the background", and your mind can use it's internal representation to render something along those lines. I feel like computers some day should be able to do the same thing, except that with another kilobyte of constraints I should be able to make the final result really close to a particular photo that matches that description.

anonfunction 9y ago
Though it has little bearing on your point, many people including myself do not possess the ability to "imagine a photo". It is known as aphantasia and Blake Ross has a great article[1] on the subject. 1. https://www.facebook.com/notes/blake-ross/aphantasia-how-it-feels-to-be-blind-in-your-mind/10156834777480504/ https://www.facebook.com/notes/blake-ross/aphantasia-how-it-...
- exikyut 9y ago
  Well, in an even more distant future, a more developed version of the kind of neural networks that the GP describes could be hooked up to your brain to provide this functionality.
arnioxux 9y ago
There was another submission recently that kind of does what you described. Click on the visualizations to see the iterations: https://dmitryulyanov.github.io/deep_image_prior https://dmitryulyanov.github.io/deep_image_prior. The ELI5 is that prior knowledge of what images look like can be used to reconstruct images w/o corruption. For non-images, this idea is pretty old and sounds a lot like compression algorithms that share a pre-defined dictionary. For example: https://en.wikipedia.org/wiki/Brotli https://en.wikipedia.org/wiki/Brotli, "improved the compression ratio by using a pre-defined dictionary of frequently-used words and phrases." Of course words/phrases are a lot easier to predefine. I wonder how large the predefined weights of the NN has to be to effectively compress real world images?

anonfunction 9y ago

Though it has little bearing on your point, many people including myself do not possess the ability to "imagine a photo". It is known as aphantasia and Blake Ross has a great article[1] on the subject. 1. https://www.facebook.com/notes/blake-ross/aphantasia-how-it-feels-to-be-blind-in-your-mind/10156834777480504/ https://www.facebook.com/notes/blake-ross/aphantasia-how-it-...

exikyut 9y ago
Well, in an even more distant future, a more developed version of the kind of neural networks that the GP describes could be hooked up to your brain to provide this functionality.

arnioxux 9y ago

There was another submission recently that kind of does what you described. Click on the visualizations to see the iterations: https://dmitryulyanov.github.io/deep_image_prior https://dmitryulyanov.github.io/deep_image_prior. The ELI5 is that prior knowledge of what images look like can be used to reconstruct images w/o corruption. For non-images, this idea is pretty old and sounds a lot like compression algorithms that share a pre-defined dictionary. For example: https://en.wikipedia.org/wiki/Brotli https://en.wikipedia.org/wiki/Brotli, "improved the compression ratio by using a pre-defined dictionary of frequently-used words and phrases." Of course words/phrases are a lot easier to predefine. I wonder how large the predefined weights of the NN has to be to effectively compress real world images?