CPC G06N 3/08 (2013.01) [G06N 3/04 (2013.01)] | 10 Claims |
1. A method of training a machine learning (ML) model using informed pseudolabels, the method performed by circuitry configured to implement a pseudolabel generator, the method comprising:
receiving previously assigned labels indicating an expected classification for data, the labels generated by a partially trained ML model in an immediately previous training epoch and the labels having a specified uncertainty;
generating the informed pseudolabels for the data based on a mathematical combination of (i) a first probability of the previously assigned labels given a class vector determined by the partially trained ML model, and (ii) a second probability of the class vector determined by the partially trained ML model given the data; and
training, by substituting the informed pseudolabels for the previously assigned labels in a next epoch of training, the partially trained ML model resulting in the ML model.
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