MoMo Strategy: Learn More from More Mistakes

Training accurate convolutional neural networks (CNNs) is essential for achieving high-performance machine learning models. However, limited training data pose a challenge, reducing model accuracy. This research investigates the selection and utilization of misclassified training samples to enhance the accuracy of CNNs where the dataset is long-tail distributed. Unlike classical resampling methods involving oversampling of tail classes and undersampling of head classes, we propose an approach that allocates more misclassified training samples into the training process to learn more (namely, MoMo strategy), with ratios of 50:50 and 70:30 for the wrongly predicted and correctly predicted samples, respectively. Additionally, we propose incorporating a balanced sample selection method, whereby the maximum training sample per class in an epoch is assigned to address the long-tail dataset problem. Our experimental results on a subset of the current largest plant dataset, PlantCLEF 2023, demonstrate an increase of 1%-2% in overall validation accuracy and a 2%-5% increase in tail class identification. By selectively focusing on more misclassified samples in training, at the same time, integrating a balanced sample selection achieves a significant boost in accuracy compared to traditional training methods. These findings emphasize the significance of adding more misclassified samples into training, encouraging researchers to rethink the sampling strategies before implementing more complex and robust network architectures and modules.