Patent ID: 11902369
Assignee: PREFERRED NETWORKS, INC.
Field: Digital communication (Electrical engineering)
Classification: CPC H  G | IPC G  H

Claim 19:
20. A method for learning a model comprising:
inputting, by processing circuitry, first data into a first neural network model to generate second data which is compressed data of the first data;
inputting, by the processing circuitry, the second data into a second neural network model to generate third data which is decompressed data of the second data, the second data including a plurality of features each regarding different resolutions; and
learning, by the processing circuitry, the first neural network model and the second neural network model based on difference between the first data and the third data,
wherein a size of the second data is smaller than a size of the first data, and the second data includes at least a feature regarding a first resolution and a feature regarding a second resolution, the second resolution is higher than the first resolution,
wherein the feature regarding the first resolution is inputted into one layer of the second neural network model and the feature regarding the second resolution is inputted into another layer of the second neural network model, the another layer being different from the one layer,
wherein the first neural network model has at least one layer and another layer which is deeper than the one layer of the first neural network model, the feature regarding the first resolution is generated based on an output of the another layer of the first neural network model, and the feature regarding the second resolution is generated based on at least a first portion of an output of the one layer of the first neural network model, and
wherein at least a second portion of the output of the one layer of the first neural network model is inputted into the another layer of the first neural network model.