Data collected from the real world typically exhibit long-tailed distributions, where frequent classes contain abundant data while rare ones have only a limited number of samples. While existing supervised learning approaches have been proposed to tackle such data imbalance, the requirement of label supervision would limit their applicability to real-world scenarios in which label annotation might not be available. Without the access to class labels nor the associated class frequencies, we propose Frequency-Aware Self-Supervised Learning (FASSL) in this paper. Targeting at learning from unlabeled data with inherent long-tailed distributions, the goal of FASSL is to produce discriminative feature representations for downstream classification tasks. In FASSL, we first learn frequency-aware prototypes, reflecting the associated long-tailed distribution. Particularly focusing on rare-class samples, the relationships between image data and the derived prototypes are further exploited with the introduced self-supervised learning scheme. Experiments on long-tailed image datasets quantitatively and qualitatively verify the effectiveness of our learning scheme.

翻译：现实世界收集的数据通常呈现长尾分布，其中高频类别包含大量数据，而低频类别仅有少量样本。尽管现有监督学习方法已提出应对此类数据不平衡问题，但标签监督的要求会限制其在标签标注不可用的实际场景中的适用性。本文在不获取类别标签及其对应类别频率的条件下，提出频率感知自监督学习（FASSL）。该方法旨在从具有固有长尾分布的无标签数据中学习，目标是为下游分类任务生成具有判别性的特征表示。在FASSL中，我们首先学习反映相关长尾分布的频率感知原型，特别针对稀有类别样本，通过引入的自监督学习机制进一步挖掘图像数据与所得原型之间的关系。在长尾图像数据集上的定性与定量实验验证了我们学习机制的有效性。