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:
Oct. 21, 2025

Filed:

Jun. 11, 2021
Applicant:

Robert Bosch Gmbh, Stuttgart, DE;

Inventors:

Didrik Nielsen, København K, DK;

Emiel Hoogeboom, Amsterdam, NL;

Kaspar Sakmann, Stuttgart, DE;

Max Welling, Amsterdam, NL;

Priyank Jaini, Amsterdam, NL;

Assignee:

ROBERT BOSCH GMBH, Stuttgart, DE;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06F 18/211 (2023.01); G06F 18/214 (2023.01); G06F 18/2415 (2023.01); G06F 18/2431 (2023.01); G06F 18/25 (2023.01); G06N 3/048 (2023.01); G06N 3/08 (2023.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/776 (2022.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); G06F 18/211 (2023.01); G06F 18/2155 (2023.01); G06F 18/2415 (2023.01); G06F 18/2431 (2023.01); G06F 18/25 (2023.01); G06N 3/048 (2023.01); G06N 3/08 (2013.01); G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G06V 10/776 (2022.01);
Abstract

A computer-implemented method of training an image classifier which uses any combination of labelled and/or unlabelled training images. The image classifier comprises a set of transformations between respective transformation inputs and transformation outputs. An inverse model is defined in which for a deterministic, non-injective transformation of the image classifier, its inverse is approximated by a stochastic inverse transformation. During training, for a given training image, a likelihood contribution for this transformation is determined based on a probability of its transformation inputs being generated by the stochastic inverse transformation given its transformation outputs. This likelihood contribution is used to determine a log-likelihood for the training image to be maximized (and its label, if the training image is labelled), based on which the model parameters are optimized.


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