US 2026/0073301 A1Published application

Machine learning model input monitor

OwnerMert Keser, Erwin Kraftinventors

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Abstract

A trained vision foundation model and a training data set used for training of the machine learning model are provided. For each training image of the training data set, a training data feature vector is determined using the vision foundation model. A distribution of the training data feature vectors is determined, An image depicting a scene is received. An image feature vector is determined for the image using the vision foundation model. A log likelihood is computed for the image feature vector to the distribution of the training data feature vectors. An alert is produced if the image differs from the distribution of the training data feature vectors based on an analysis of the log likelihood.

Claims13

Dependent claims

A method, in particular a computer-implemented method, for monitoring the performance of a trained machine learning model, the method comprising:

providing a trained vision foundation model;
providing a training data set used for training of the machine learning model;
for each training image of the training data set determining a training data feature vector using the vision foundation model,
determining a distribution of the training data feature vectors;
receiving an image depicting a scene;
determining an image feature vector for the image using the vision foundation model;
computing a log likelihood for the image feature vector to the distribution of the training data feature vectors;
providing an alert if the image differs from the distribution of the training data feature vectors based on an analysis of the log likelihood.

5 more elements

Dependencies are inferred from the claim text.