The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 11, 2025

Filed:

Feb. 19, 2021
Applicant:

Robert Bosch Gmbh, Stuttgart, DE;

Inventors:

Jorn Peters, Amsterdam, NL;

Thomas Andy Keller, Amsterdam, NL;

Anna Khoreva, Stuttgart, DE;

Max Welling, Amsterdam, NL;

Priyank Jaini, Amsterdam, NL;

Assignee:

ROBERT BOSCH GMBH, Stuttgart, DE;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/082 (2023.01); G05D 1/00 (2024.01); G06F 18/2413 (2023.01); G06N 3/008 (2023.01); G06N 3/04 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01);
U.S. Cl.
CPC ...
G06N 3/082 (2013.01); G06F 18/2414 (2023.01); G06N 3/04 (2013.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01); G05D 1/0088 (2013.01); G06N 3/008 (2013.01);
Abstract

A method for training a neural network. The neural network comprises a first layer which includes a plurality of filters to provide a first layer output comprising a plurality of feature maps. Training of the classifier includes: receiving, by a preceding layer, a first layer input in the first layer, wherein the first layer input is based on the input signal; determining the first layer output based on the first layer input and a plurality of parameters of the first layer; determining a first layer loss value based on the first layer output, wherein the first layer loss value characterizes a degree of dependency between the feature maps, the first layer loss value being obtained in an unsupervised fashion; and training the neural network. The training includes an adaption of the parameters of the first layer, the adaption being based on the first layer loss value.


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