Which of the following modes increases the coverage of the dataset?

Low Confidence mode is designed to enhance the coverage of the dataset by focusing on samples that are less certain or are frequently misclassified by the model. This mode encourages the training process to pay more attention to these uncertain areas, thereby improving the model's ability to handle edge cases or rare instances within the dataset. This results in a more robust pretrained model that can generalize better to new, unseen data.

In contrast, Shuffle mode typically alters the order in which training data is presented but does not inherently increase dataset coverage. Train Label mode is primarily concerned with assigning labels to training data rather than expanding or improving coverage. Rebalance mode aims to adjust the distribution of classes within the dataset, ensuring that all classes are represented more evenly, but it does not focus on the model's confidence regarding its predictions. Each of these other modes has its purpose, but none specifically target the increase of dataset coverage like Low Confidence mode does.