| CPC G06N 3/088 (2013.01) [G06N 3/045 (2023.01); G06N 3/047 (2023.01)] | 20 Claims |
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1. A method comprising:
receiving, by a processor, an input dataset;
generating, by the processor, at least first and second instances of a first classifier, wherein the first instance of the first classifier is initialized to a first state, and the second instance of the first classifier is initialized to a second state different from the first state;
training, by the processor, the first and second instances of the first classifier based on the input dataset;
training, by the processor, a second classifier based on the input dataset, wherein the second classifier is configured to learn a representation of the input dataset in a latent space;
invoking, by the processor, the second classifier for generating a first supplemental dataset in the latent space;
generating, by the processor, a first prediction for labeling the first supplemental dataset based on the first instance of the first classifier;
generating, by the processor, a second prediction for labeling the first supplemental dataset based on the second instance of the first classifier;
generating, by the processor, annotations for the first supplemental dataset based on the first prediction and the second prediction, and outputting an annotated first supplemental dataset; and
training, by the processor, a third classifier based on at least the input dataset and the annotated first supplemental dataset.
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