Kalibrierung von Klassifikationsmodellen

Details

Veröffentlichungs-Nr.

EP3913538

Aktenzeichen

EP20175680

Anmeldetag

20. Mai 2020

Veröffentlichung

24. Dezember 2025

Erteilung

24. Dezember 2025

Rechtsraum

EP

IPC

G06N3/08G06N3/047

Abstract

A computer-implemented method (500) of calibrating a trained classification model is described. The trained classification model is trained to classify input samples according to a plurality of classes and to provide associated prediction probabilities, and comprises a plurality of hidden layers and at least one activation layer. The method comprises accessing (510) the trained classification model and accessing (510) a plurality of validation samples, each validation sample having a ground-truth label, the ground-truth label indicating a ground-truth class. The method further comprises applying (520) the trained classification model to the plurality of validation samples, obtaining (530), for each validation sample, an output logit vector from a layer of the trained classification model preceding a last activation layer, and training (540) a calibration module. The calibration module is trained to adjust prediction probabilities, the prediction probabilities being derived from the output logit vectors. The calibration module comprises at least one of a finetuning submodule for adjusting the prediction probabilities by finetuning the output logit vector and a binning submodule for adjusting the prediction probabilities by binning the output logit vector. The method further comprises appending (570) the trained calibration module to the trained classification model to obtain a calibrated classification model.

Anmelder

Firma

Robert Bosch GmbH

Land

🇩🇪 Deutschland

Fachgebiete