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:
Aug. 04, 2026

Filed:

Sep. 28, 2022
Applicant:

Dell Products L.p., Round Rock, TX (US);

Inventors:

Zijia Wang, WeiFang, CN;

Jiacheng Ni, Shanghai, CN;

Jinpeng Liu, Shanghai, CN;

Zhen Jia, Shanghai, CN;

Kenneth Durazzo, Morgan Hill, CA (US);

Assignee:

Dell Products L.P., Round Rock, TX (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/045 (2023.01); G06N 3/088 (2023.01);
U.S. Cl.
CPC ...
G06N 3/045 (2023.01); G06N 3/088 (2013.01);
Abstract

An apparatus comprises a processing device configured to train first and second machine learning models utilizing a first training dataset comprising inputs each associated with a class label of one of a set of classes and a second training dataset comprising distilled representations of the two or more classes, and to identify candidate adversarial example inputs utilizing the trained first and second machine learning models. The processing device is further configured to determine whether the candidate adversarial example inputs are true positive adversarial example inputs based on a confidence-aware clustering and to generate an updated first training dataset comprising corrected class labels for the true positive adversarial example inputs and an updated second training dataset comprising updated distilled representations determined utilizing the corrected class labels. The processing device is further configured to re-train the first and second machine learning models utilizing the updated first and second training datasets.


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