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SYSTEMS AND METHODS FOR FEW-SHOT TRANSFER LEARNING

  • US 20200130177A1
  • Filed: 08/05/2019
  • Published: 04/30/2020
  • Est. Priority Date: 10/29/2018
  • Status: Active Application
First Claim
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1. A method for training a controller to control a robotic system in a target domain, the method comprising:

  • receiving a neural network of an original controller for controlling the robotic system based on a plurality of origin data samples from an origin domain and corresponding labels in a label space the neural network of the original controller comprising a plurality of encoder parameters and a plurality of classifier parameters, the neural network being trained to;

    map an input data sample from the origin domain to a feature vector in a feature space in accordance with the encoder parameters; and

    assign a label of the label space to the input data sample based on the feature vector in accordance with the classifier parameters;

    updating the encoder parameters to minimize a dissimilarity, in the feature space, between;

    a plurality of origin feature vectors computed from the origin data samples; and

    a plurality of target feature vectors computed from a plurality of target data samples from the target domain, the target data samples having a smaller cardinality than the origin data samples; and

    updating the controller with the updated encoder parameters to control the robotic system in the target domain.

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