CPC G06F 40/42 (2020.01) [G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06F 40/58 (2020.01); G06N 3/088 (2013.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 20/10 (2022.01); G06V 20/58 (2022.01)] | 17 Claims |
1. A computer implemented method comprising:
receiving a reference training dataset including one or more reference training samples;
accessing a training dataset generator model configured to generate training datasets;
accessing a learner model configured to process the generated training datasets and the reference training dataset;
in one or more iterations:
generating, by the training dataset generator model, a training dataset according to noise as input, the training dataset including one or more training samples generated by the training dataset generator model;
training the learner model using the generated training dataset by determining a first loss based on a difference between an output value predicted by the learner model and a corresponding output value generated by the training dataset generator model, wherein parameters of the learner model are adjusted based on the first loss;
determining a second loss based on execution of the trained learner model using the reference training dataset; and
adjusting parameters of the training dataset generator model based on the second loss determined based on execution of the trained learner model using the reference training dataset;
storing one or more training datasets generated by the training dataset generator model; and
storing the training dataset generator model with adjusted parameters and the trained learner model.
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