Deep convolutional neural networks often perform poorly when faced with datasets that suffer from quantity imbalances and classification difficulties. Despite advances in the field, existing two-stage approaches still exhibit dataset bias or domain shift. To counter this, a phased progressive learning schedule has been proposed that gradually shifts the emphasis from representation learning to training the upper classifier. This approach is particularly beneficial for datasets with larger imbalances or fewer samples. Another new method a coupling-regulation-imbalance loss function is proposed, which combines three parts: a correction term, Focal loss, and LDAM loss. This loss is effective in addressing quantity imbalances and outliers, while regulating the focus of attention on samples with varying classification difficulties. These approaches have yielded satisfactory results on several benchmark datasets, including Imbalanced CIFAR10, Imbalanced CIFAR100, ImageNet-LT, and iNaturalist 2018, and can be easily generalized to other imbalanced classification models.

翻译：深度卷积神经网络在处理存在数量不平衡和分类难度的数据集时通常表现不佳。尽管该领域已有进展，现有的两阶段方法仍存在数据集偏差或领域偏移问题。为此，本文提出了一种分阶段渐进式学习策略，该策略逐步将学习重点从表示学习转移到上层分类器的训练上。这一方法对具有较大不平衡性或较少样本的数据集尤为有利。此外，还提出了一种新型的耦合-调节-不平衡损失函数，该函数由三个部分组成：修正项、Focal损失和LDAM损失。该损失函数能有效应对数量不平衡和异常值问题，同时调节对分类难度不同样本的关注程度。这些方法在多个基准数据集（包括不平衡CIFAR10、不平衡CIFAR100、ImageNet-LT和iNaturalist 2018）上取得了令人满意的结果，并可轻松推广到其他不平衡分类模型。