ACTIVE LEARNING VIA A SAMPLE CONSISTENCY ASSESSMENT: abstract simplified (18333998)

• This abstract for appeared for patent application number 18333998 Titled 'ACTIVE LEARNING VIA A SAMPLE CONSISTENCY ASSESSMENT'

Simplified Explanation

This method involves using a machine learning model to generate predictions for a set of unlabeled training samples. The predictions are compared to modified versions of the training samples to determine the difference. Based on these differences, a subset of the training samples is selected. Ground truth labels are obtained for the samples in the subset, and labeled training samples are generated using these labels. Finally, the machine learning model is trained using these labeled training samples.

Original Abstract Submitted

A method includes obtaining a set of unlabeled training samples. For each training sample in the set of unlabeled training samples generating, the method includes using a machine learning model and the training sample, a corresponding first prediction, generating, using the machine learning model and a modified unlabeled training sample, a second prediction, the modified unlabeled training sample based on the training sample, and determining a difference between the first prediction and the second prediction. The method includes selecting, based on the differences, a subset of the set of unlabeled training samples. For each training sample in the subset of the set of unlabeled training samples, the method includes obtaining a ground truth label for the training sample, and generating a corresponding labeled training sample based on the training sample paired with the ground truth label. The method includes training the machine learning model using the corresponding labeled training samples.