The Long-Tailed Recognition (LTR) problem emerges in the context of learning from highly imbalanced datasets, in which the number of samples among different classes is heavily skewed. LTR methods aim to accurately learn a dataset comprising both a larger Head set and a smaller Tail set. We propose a theorem where under the assumption of strong convexity of the loss function, the weights of a learner trained on the full dataset are within an upper bound of the weights of the same learner trained strictly on the Head. Next, we assert that by treating the learning of the Head and Tail as two separate and sequential steps, Continual Learning (CL) methods can effectively update the weights of the learner to learn the Tail without forgetting the Head. First, we validate our theoretical findings with various experiments on the toy MNIST-LT dataset. We then evaluate the efficacy of several CL strategies on multiple imbalanced variations of two standard LTR benchmarks (CIFAR100-LT and CIFAR10-LT), and show that standard CL methods achieve strong performance gains in comparison to baselines and approach solutions that have been tailor-made for LTR. We also assess the applicability of CL techniques on real-world data by exploring CL on the naturally imbalanced Caltech256 dataset and demonstrate its superiority over state-of-the-art classifiers. Our work not only unifies LTR and CL but also paves the way for leveraging advances in CL methods to tackle the LTR challenge more effectively.

翻译：长尾识别问题源于从高度不平衡数据集中学习的情境，其中不同类别的样本数量严重偏斜。长尾识别方法旨在精确学习包含较大头部集和较小尾部集的数据集。我们提出一个定理：在损失函数强凸性的假设下，在全数据集上训练的模型权重，将严格限定在仅对头部数据训练所得权重的上界内。接着，我们论证通过将头部和尾部的学习视为两个独立且连续的步骤，持续学习方法能够有效更新模型权重以学习尾部数据，同时避免遗忘头部知识。首先，我们在简易MNIST-LT数据集上通过多种实验验证理论发现。随后，在两个标准长尾识别基准（CIFAR100-LT和CIFAR10-LT）的多种不平衡变体上评估多种持续学习策略的有效性，结果表明标准持续学习方法相较于基线模型取得了显著的性能提升，并能逼近专为长尾识别定制的解决方案。我们还通过在天然不平衡的Caltech256数据集上探索持续学习，评估其在真实数据上的适用性，并证明其优于现有最先进的分类器。我们的工作不仅统一了长尾识别与持续学习，更为利用持续学习方法的进步来更有效应对长尾识别挑战铺平了道路。