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Except that in my understand, the decoded version looks like shit. So, while optimistic that this technology might be quite good eventually, more heuristics ar

Except that in my understand, the decoded version looks like shit. So, while optimistic that this technology might be quite good eventually, more heuristics are probably needed for the right way to extract an optimal encoding. e.g. see the screenshots here: http://www.eteknix.com/blade-runner-gets-trippy-auto-encoded-ai/ http://www.eteknix.com/blade-runner-gets-trippy-auto-encoded...

Mithaldu 10y ago
This is only a proof of concept, with a very small input size, and very strong compression. The impressive thing is that it works at all.
- radarsat1 10y ago
  Right, but my point is that your numbers don't indicate anything about the actual potential as a compression format. It is certainly possible to use it that way, but there is still much that is uncertain about whether a neural network can do better than hand-crafted compression formats. So it's a little early to call it "next generation." It is literally meaningless to point out that "his neural network compresses each image down to 200 floats", since it doesn't actually work well with such a small latent space. It's not exactly unknown that autoencoders can reconstruct images, so I don't see why it's "impressive that it works at all."
  - Mithaldu 10y ago
    > So it's a little early to call it "next generation." Please don't ignore words other people write, or at least reread before hitting post. I did say possibly. > I don't see why it's "impressive that it works at all." I thought it was implicit from my explanation that the "works at all" includes the fact that the data sizes are reasonable already. If we had the same result, but the intermediary data was 18 gigabytes, then there would be nothing impressive about it. As it is, we're a lot below that, before further compression, so it is. Remember: Prototype, Proof of Concept. You're looking at the first step, not the last step. You're looking at a motor carriage, not a Tesla.
    - radarsat1 10y ago
      > I thought it was implicit from my explanation that the "works at all" includes the fact that the data sizes are reasonable already. Right, but why is that impressive if it doesn't actually result in a good reconstruction? I can take any collection of numbers and summarize it with the mean value, that doesn't imply that averaging is a good compression method. If you consider this proof of concept, then what, exactly, concept does it prove? That statistics can represent a dataset?
lallysingh 10y ago
As part of a compression suite, that may not matter all that much. I can think of a hybridized version of this that starts with this ANN and then applies 'fixes' by a traditional video codec stream. If the ANN can represent most of the bits of the frame pretty well, the ANN + Codec data can match a traditional codec-only approach with substantially less data.
- joe_the_user 10y ago
  The thing is that diff between the film and "encoded" version look pretty fuzzy/muddy and so compressing that diff would have to be difficult. Anyway, the article doesn't mention this idea, the first hn discussion had someone claim some other ridiculous use of the fuzzy/muddy images that are supposed to mean something and ultimately I'd say it's an example of "my research project has cool images, I can use them for publicity".
tripzilch 10y ago
This network was trained on a single NVidia 960 GPU for two weeks, I wonder how much better it could have been with more computing power thrown at it?
Houshalter 10y ago
You would store the diff between the reconstructed version and the original. The diff could be compressed to much less space than the original image, because the NN can predict most of the pixels. And get within a few bits of the rest.

Mithaldu 10y ago

This is only a proof of concept, with a very small input size, and very strong compression. The impressive thing is that it works at all.

radarsat1 10y ago
Right, but my point is that your numbers don't indicate anything about the actual potential as a compression format. It is certainly possible to use it that way, but there is still much that is uncertain about whether a neural network can do better than hand-crafted compression formats. So it's a little early to call it "next generation." It is literally meaningless to point out that "his neural network compresses each image down to 200 floats", since it doesn't actually work well with such a small latent space. It's not exactly unknown that autoencoders can reconstruct images, so I don't see why it's "impressive that it works at all."
- Mithaldu 10y ago
  > So it's a little early to call it "next generation." Please don't ignore words other people write, or at least reread before hitting post. I did say possibly. > I don't see why it's "impressive that it works at all." I thought it was implicit from my explanation that the "works at all" includes the fact that the data sizes are reasonable already. If we had the same result, but the intermediary data was 18 gigabytes, then there would be nothing impressive about it. As it is, we're a lot below that, before further compression, so it is. Remember: Prototype, Proof of Concept. You're looking at the first step, not the last step. You're looking at a motor carriage, not a Tesla.
  - radarsat1 10y ago
    > I thought it was implicit from my explanation that the "works at all" includes the fact that the data sizes are reasonable already. Right, but why is that impressive if it doesn't actually result in a good reconstruction? I can take any collection of numbers and summarize it with the mean value, that doesn't imply that averaging is a good compression method. If you consider this proof of concept, then what, exactly, concept does it prove? That statistics can represent a dataset?

lallysingh 10y ago

As part of a compression suite, that may not matter all that much. I can think of a hybridized version of this that starts with this ANN and then applies 'fixes' by a traditional video codec stream. If the ANN can represent most of the bits of the frame pretty well, the ANN + Codec data can match a traditional codec-only approach with substantially less data.

joe_the_user 10y ago
The thing is that diff between the film and "encoded" version look pretty fuzzy/muddy and so compressing that diff would have to be difficult. Anyway, the article doesn't mention this idea, the first hn discussion had someone claim some other ridiculous use of the fuzzy/muddy images that are supposed to mean something and ultimately I'd say it's an example of "my research project has cool images, I can use them for publicity".

tripzilch 10y ago

This network was trained on a single NVidia 960 GPU for two weeks, I wonder how much better it could have been with more computing power thrown at it?

Houshalter 10y ago

You would store the diff between the reconstructed version and the original. The diff could be compressed to much less space than the original image, because the NN can predict most of the pixels. And get within a few bits of the rest.