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It's pretty easy to build a lossless learned encoder. Encode the image with your model, then apply a lossless encoding to the error. You can also include a meas
by sdenton4 7y ago
It's pretty easy to build a lossless learned encoder. Encode the image with your model, then apply a lossless encoding to the error. You can also include a measure of the entropy of the error in your model's loss function.
The model should make the errors small, and including the entropy measure of the error in the loss function means that the model produces "cheaper to correct" errors.
A nice keyword on this front is "bits back argument".
- barrkel 7y agoThat's a nice theory, but what if your ML encoder uses textures to encode perceptual equivalence efficiently that's actually got very high errors measured by pixel? It seems to me the best ML-driven encoders would work by inferring higher abstraction details from the image in ways which are hard to get back to lossless - that there's an intrinsic tension there.
- fwip 7y agoThen choose a different encoder, or add another layer to shift/correct the texture.