An Efficient Sample Utilization Method for Deep Learning Based on Class Uncertainty

Deep learning has achieved success across many domains when sufficient training samples are available. However, the commonly used mini-batch stochastic gradient descent (SGD) training paradigm treats each sample equally, resulting in massive computational waste on samples that are easily identifiable. In contrast, low-quality samples, such as those with erroneous labels, can negatively impact the training process. To address these issues, we propose a training sample utilization method based on sample uncertainty. Once the model has acquired preliminary decision-making abilities, the class uncertainty for each sample can be evaluated within a training epoch. Subsequently, the samples are probabilistically selected based on their uncertainty for the next epoch. Experiments conducted on the GSC v2 and CIFAR-10 datasets demonstrate that the proposed method can reduce training time by over 32% and 58%, respectively, with only a loss of 1% performance. Additionally, the method has the capability to mitigate the adverse effects of samples with erroneous labels.