System, method and apparatus for training a machine learning model

A system for training a machine learning model comprising:

• one or more computers;
• and
• one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
• receiving a training data set for training the machine learning model to perform a machine learning task, the training data set comprising a plurality of original training samples;
• augmenting the original training samples by applying default transformations stored in a default augmentation transformations list and storing the augmented training samples as a first set of augmented training samples in the training data set;
• training the machine learning model on at least a portion of the original training samples and at least a portion of the first set of augmented training samples;
• computing an unaugmented accuracy of the machine learning model trained on at least a portion of the original training samples and at least a portion of the first set of augmented training samples;
• augmenting the original training samples and the first set of augmented training samples by applying a candidate transformation selected from a candidate augmentation transformations list and storing the augmented training samples as a second set of augmented training samples in the training data set;
• training the machine learning model on at least a portion of the original training samples, at least a portion of the first set of augmented training samples, and at least a portion of the second set of augmented training samples;
• computing an augmented accuracy of the machine learning model trained on at least a portion of the original training samples, at least a portion of the first set of augmented training samples, at least a portion of the second set of augmented training samples;
• computing an affinity metric from the unaugmented accuracy and the augmented accuracy;
• updating the candidate augmentation transformations list and the default augmentation transformations list in accordance with the affinity metric;
• removing the second set of augmented training samples from the training data set;
• augmenting the original training samples and the first set of augmented training samples in accordance with transformations in the updated candidate augmentation transformations list and the updated default augmentation transformations list and storing the augmented training samples as a third set of augmented training samples in the training data set;
• and
• training the machine learning model on at least a portion of the original training samples, at least a portion of the first set of augmented training samples, and at least a portion of the third set of augmented training samples to perform the machine learning task.

One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations for training a machine learning model comprising:

• receiving a training data set for training the machine learning model to perform a machine learning task, the training data set comprising a plurality of original training samples;
• augmenting the original training samples by applying default transformations stored in a default augmentation transformations list and storing the augmented training samples as a first set of augmented training samples in the training data set;
• training the machine learning model on at least a portion of the original training samples and at least a portion of the first set of augmented training samples;
• computing an unaugmented accuracy of the machine learning model trained on at least a portion of the original training samples and at least a portion of the first set of augmented training samples;
• augmenting the original training samples and the first set of augmented training samples by applying a candidate transformation selected from a candidate augmentation transformations list and storing the augmented training samples as a second set of augmented training samples in the training data set;
• training the machine learning model on at least a portion of the original training samples, at least a portion of the first set of augmented training samples, and at least a portion of the second set of augmented training samples;
• computing an augmented accuracy of the machine learning model trained on at least a portion of the original training samples, at least a portion of the first set of augmented training samples, at least a portion of the second set of augmented training samples;
• computing an affinity metric from the unaugmented accuracy and the augmented accuracy;
• updating the candidate augmentation transformations list and the default augmentation transformations list in accordance with the affinity metric;
• removing the second set of augmented training samples from the training data set;
• augmenting the original training samples and the first set of augmented training samples in accordance with transformations in the updated candidate augmentation transformations list and the updated default augmentation transformations list and storing the augmented training samples as a third set of augmented training samples in the training data set;
• and
• training the machine learning model on at least a portion of the original training samples, at least a portion of the first set of augmented training samples, and at least a portion of the third set of augmented training samples to perform the machine learning task.