Generating training datasets for training neural networks

A computer system comprising:

• one or more processors;
• and
• a non-transitory computer readable storage medium comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform operations 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.

A non-transitory computer readable storage medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations 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.