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:
Apr. 16, 2024

Filed:

Aug. 16, 2021
Applicant:

Robert Bosch Gmbh, Stuttgart, DE;

Inventors:

Jorn Peters, Amsterdam, NL;

Thomas Andy Keller, Amsterdam, NL;

Anna Khoreva, Stuttgart, DE;

Emiel Hoogeboom, Amsterdam, NL;

Max Welling, Amsterdam, NL;

Priyank Jaini, Amsterdam, NL;

Assignee:

ROBERT BOSCH GMBH, Stuttgart, DE;

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06V 10/32 (2022.01); G06F 18/214 (2023.01); G06F 18/2321 (2023.01); G06V 10/46 (2022.01); G06V 10/75 (2022.01);
U.S. Cl.
CPC ...
G06V 10/32 (2022.01); G06F 18/214 (2023.01); G06F 18/2321 (2023.01); G06V 10/46 (2022.01); G06V 10/7515 (2022.01); G06V 10/473 (2022.01);
Abstract

A computer-implemented method for training a normalizing flow. The normalizing flow predicts a first density value based on a first input image. The first density value characterizes a likelihood of the first input image to occur. The first density value is predicted based on an intermediate output of a first convolutional layer of the normalizing flow. The intermediate output is determined based on a plurality of weights of the first convolutional layer. The method for training includes: determining a second input image; determining an output, wherein the output is determined by providing the second input image to the normalizing flow and providing an output of the normalizing flow as output; determining a second density value based on the output tensor and on the plurality of weights; determining a natural gradient of the plurality of weights with respect to the second density value; adapting the weights according to the natural gradient.


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