Vorrichtung und Verfahren zum Trainieren eines Normalisierungsstroms unter Verwendung von Selbstnormalisierten Gradienten

Details

Veröffentlichungs-Nr.

EP3975011

Aktenzeichen

EP20199040

Anmeldetag

29. September 2020

Veröffentlichung

5. November 2025

Erteilung

5. November 2025

Rechtsraum

EP

IPC

G06F17/15G06F17/18G06N3/04G06N3/08

Abstract

Computer-implemented method for training a normalizing flow (60), wherein the normalizing flow (60) is configured to determine a first output signal (y) characterizing a likelihood or a log-likelihood of an input signal (x), wherein the normalizing flow (60) comprises at least one first layer, wherein the first layer comprises trainable parameters and a layer input to the first layer is based on the input signal (x) and the first output signal (y) is based on a layer output of the first layer, wherein training the normalizing flow comprises the steps of: • Determining at least one training input signal (x<i>); • Determining a training output signal (y<i>) for each training input signal (x<i>) by means of the normalizing flow (60); • Determining a first loss value, wherein the first loss value is based on a likelihood or a log-likelihood of the at least one determined training output signal (y<i>) with respect to a predefined probability distribution; • Determining an approximation of a gradient of the trainable parameters of the first layer with respect to the first loss value, wherein the gradient is dependent on an inverse of a matrix of the trainable parameters and determining the approximation of the gradient is achieved by optimizing an approximation of the inverse; • Updating the trainable parameters of the first layer based on the approximation of the gradient.

Anmelder

Firma

Robert Bosch GmbH

Land

🇩🇪 Deutschland

Fachgebiete