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When I referred to sampling I was talking about the joint distribution of all pixels (hence "space of images"), which would work fine. But I suspect predicting
by darkmighty 11y ago
When I referred to sampling I was talking about the joint distribution of all pixels (hence "space of images"), which would work fine. But I suspect predicting distributions is impractical, it may be better to use methods that sample directly without ever explicitly finding the distribution.
You do need a source of entropy to perform the sampling. This amount should be more than a minimum given by how precisely you want to sample from the continuous source, related to the Kullback-Leibler divergence of the distribution.
- Houshalter 11y agoWell in theory, the adversarial nets should learn to model the distribution perfectly. But there might be a way to do it directly. You could train an NN to produce samples from a random input source, just like the adversarial nets. But unlike the adversarial nets, the inputs don't need to be random. You could train another NN to predict a distribution of what they should be. And then instead of adversarial training, just regular training to predict the exact pixels, and backproping all the way through.