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Activation layers for deep learning networks

  • US 9,892,344 B1
  • Filed: 11/30/2015
  • Issued: 02/13/2018
  • Est. Priority Date: 11/30/2015
  • Status: Active Grant
First Claim
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1. A computer-implemented method, comprising:

  • obtaining a convolutional neural network containing at least one convolutional layer and at least one activation layer, the at least one activation layer including a generalized linear unit, the generalized linear unit having a functional form described using a pair of straight lines with three parameters including a first slope in a positive region, a second slope in a negative region, and an offset applied to the first slope and the second slope, the three parameters learnable over at least one input channel;

    training the convolutional neural network using a first subset of a training data set, the training data set including instances of image data with defined labels;

    measuring accuracy of the convolutional neural network after the training by classifying images in a test set;

    receiving a query image from a client device associated with a user;

    processing the query image using the convolutional neural network to determine a classification of an object represented in the query image;

    determining a set of items corresponding to the classification; and

    providing, to the client device, information for at least a subset of the set of items.

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