Dividing and Merging for Imbalanced Long-Tail Datasets

In this work, we introduce a novel approach for addressing class imbalance in long-tail datasets, focusing on the strategy of dividing and merging. Our methodology involves dividing the dataset into separate clusters, each representing a different segment of the class distribution. By training dedicated models for each cluster and subsequently merging their parameters, we achieve a more balanced learning process across diverse class sizes.