| CPC G06V 10/255 (2022.01) [G06N 3/045 (2023.01); G06N 20/20 (2019.01); G06V 10/82 (2022.01)] | 19 Claims |
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1. A method comprising:
applying a first machine learning model to an input to generate a first model output, wherein the first machine learning model has been trained on a first set of training examples;
determining, based on the first model output, a correctness metric for the first model output;
determining that the correctness metric exceeds a threshold, wherein the threshold has a value that has been determined for the first machine learning model and a second machine learning model based on a set of training inputs; and
responsive to determining that the correctness metric exceeds the threshold:
applying the second machine learning model to the input to generate a second model output, wherein the second machine learning model has been trained by (i) selecting, from the first set of training examples, a second set of training examples that, when applied to the first machine learning model, result in the generation of outputs corresponding to sub-threshold correctness metric values, and (ii) training the second machine learning model using the second set of training examples; and
combining the first model output and the second model output to generate a combined output, wherein the combining comprises at least one of: summing of the first model output and the second model output, taking the mean of the first model output and the second model output, taking a mode of the first model output and the second model output, taking an average of the first model output and the second model output, taking a weighted average of the first model output and the second model output, performing an elementwise operation on the first model output and the second model output, voting based on the first model output and the second model output, or summing logit values of the first model output and logit values of the second model output.
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