Patent ID: 11880432
Assignee: KONINKLIJKE PHILIPS N.V.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method for obtaining a confidence measure for a machine learning model, the method comprising:
processing input data with the machine learning model to generate a primary result;
generating a plurality of modified instances of the input data;
processing the plurality of modified instances of the input data with the machine learning model to generate a respective plurality of secondary results; and
determining a confidence measure relating to the primary result based on the secondary results,
wherein determining a confidence measure comprises:
determining a measure of distribution or variance of the secondary results; and
determining a confidence measure based on the determined measure of distribution or variance,
wherein determining the measure of distribution or variance of the secondary results comprises determining at least one of:
the inverse variance of the secondary results;
the Shannon entropy of the secondary results;
the gini coefficient of the secondary results;
the Kullback-Liebler divergence of the secondary results; and
a concentration measure of the secondary results,

wherein generating a plurality of modified instances of the input data comprises:
applying a first spatial warping transformation to the input data to generate a first modified instance of the input data,
applying a first inverse spatial warping transformation to the secondary result generated for the first modified instance of the input data.