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Thanks for posting this. This looks like something genuinely new. Going to look into it.

• DoctorOetker 8y ago
  I didn't read the referenced 2017 paper yet, but mapping the training data to noise (gaussian and/or other) is exactly what the RevNet paper does, with the advantage of deterministic reversibility such that the trained RevNet is also generative (without having to do gradient descent for each generated image)
  • dtjohnnyb 8y ago
    The intro to the paper has a nice comparison to other similar methods (generative and non-generative) and the blog post linked in this article by inFERNCe https://www.inference.vc/unsupervised-learning-by-predicting-noise-an-information-maximization-view-2 https://www.inference.vc/unsupervised-learning-by-predicting... has a nice comparison at the end to different unsupervised methods and where this method adds novelty (or doesn't!)
    • DoctorOetker 8y ago
      >has a nice comparison at the end to different unsupervised methods I don't see the comparisons at the end of the inFERENCe link?
• smittywerben 8y ago
  > looks like something genuinely new I fear those words
• zygotic12 8y ago
  I think I'm missing the point? A visual proof that neural nets can compute any function: http://neuralnetworksanddeeplearning.com/chap4.html http://neuralnetworksanddeeplearning.com/chap4.html