US 12,406,478 B2
Generating strong labels for examples labelled with weak labels
Klára Janousková, Prague (CZ); Ioana Giurgiu, Zurich (CH); Mattia Rigotti, Basel (CH); and Adelmo Cristiano Innocenza Malossi, Schönenberg (CH)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Feb. 8, 2023, as Appl. No. 18/166,095.
Prior Publication US 2024/0265676 A1, Aug. 8, 2024
Int. Cl. G06V 10/774 (2022.01); G06N 3/0895 (2023.01); G06V 10/77 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01)
CPC G06V 10/774 (2022.01) [G06N 3/0895 (2023.01); G06V 10/7715 (2022.01); G06V 10/7788 (2022.01); G06V 10/82 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method of generating strong labels for examples labelled with weak labels, the method comprising:
given a machine learning (ML) model trained on a training set of examples labelled according to one or more previously identified weak labels, processing a set of test examples by:
executing the trained ML model on an example of the set of test examples to infer a weak label,
extracting, from the executed ML model, explanatory features that have contributed to infer the weak label using an extraction process based on an explainability method, and
generating a strong label based on the extracted explanatory features;
prompting, via a graphical user interface, a user to react to one or each of the inferred weak label and the generated strong label, to obtain a human response; and
interpreting the human response to generate a further weak label for the example.