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NEURAL NETWORK TRAINING UTILIZING SPECIALIZED LOSS FUNCTIONS

  • US 20200134382A1
  • Filed: 11/02/2018
  • Published: 04/30/2020
  • Est. Priority Date: 10/31/2018
  • Status: Abandoned Application
First Claim
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1. A method, comprising:

  • receiving, by a computer system, a training dataset comprising a plurality of images, wherein each image of the training dataset is associated with an identifier of a class of a set of classes;

    computing, by a neural network, a plurality of feature vectors, wherein each feature vector of the plurality of feature vectors represents an image of the training dataset in a space of image features;

    computing, for the training dataset, a value of a loss function reflecting a plurality of probabilities, wherein each probability of the plurality of probabilities characterizes a hypothesis associating an image of the training dataset with a class associated with the image by the training dataset, wherein the loss function further reflects a plurality of distances, wherein each distance of the plurality of distances is computed in the space of image features between a feature vector representing an image of the training dataset and a center of a class associated with the image by the training dataset; and

    adjusting one or more parameters of the neural network based on the value of the loss function.

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