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Systems and methods for training neural networks for regression without ground truth training samples

  • US 10,565,686 B2
  • Filed: 11/08/2017
  • Issued: 02/18/2020
  • Est. Priority Date: 06/12/2017
  • Status: Active Grant
First Claim
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1. A computer-implemented method for training a neural network, comprising:

  • selecting an input sample from a set of training data that includes input samples and noisy target samples, wherein the input samples and the noisy target samples each correspond to a latent, clean target sample;

    processing the input sample by a neural network model to produce an output;

    selecting a noisy target sample from the set of training data, wherein the noisy target samples have a distribution relative to the latent, clean target sample; and

    adjusting parameter values of the neural network model to reduce differences between the output and the noisy target sample.

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