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:
Jan. 21, 2025

Filed:

Mar. 18, 2022
Applicants:

Robert Bosch Gmbh, Stuttgart, DE;

Carnegie Mellon University, Pittsburgh, PA (US);

Inventors:

Fatemeh Sheikholeslami, Pittsburgh, PA (US);

Wan-Yi Lin, Wexford, PA (US);

Jan Hendrik Metzen, Boeblingen, DE;

Huan Zhang, Pittsburgh, PA (US);

Jeremy Kolter, Pittsburgh, PA (US);

Assignees:

Robert Bosch GmbH, , DE;

Carnegie Mellon University, Pittsburgh, PA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/764 (2022.01); G06T 5/70 (2024.01); G06V 10/84 (2022.01);
U.S. Cl.
CPC ...
G06V 10/764 (2022.01); G06T 5/70 (2024.01); G06V 10/84 (2022.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A system includes a machine-learning network. The network includes an input interface configured to receive input data from a sensor. The processor is programmed to receive the input data, generate a perturbed input data set utilize the input data, wherein the perturbed input data set includes perturbations of the input data, denoise the perturbed input data set utilizing a denoiser, wherein the denoiser is configured to generate a denoised data set, send the denoised data set to both a pre-trained classifier and a rejector, wherein the pre-trained classifier is configured to classify the denoised data set and the rejector is configured to reject a classification of the denoised data set, train, utilizing the denoised input data set, the a rejector to achieve a trained rejector, and in response to obtaining the trained rejector, output an abstain classification associated with the input data, wherein the abstain classification is ignored for classification.


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