# Frequency-Aware Self-Supervised Long-Tailed Learning Source: [Frequency-Aware Self-Supervised Long-Tailed Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:44:59.954Z Authors: Ci-Siang Lin, Min-Hung Chen, Yu-Chiang Frank Wang Published: 2023-09-09T08:57:40.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on Cifar100-LT exponential](): Image Classification; Cifar100-LT exponential; Accuracy: 55.27. - [Image Classification on Cifar10-LT exponential](): Image Classification; Cifar10-LT exponential; Accuracy: 80.69. ## Original sources and implementation [Original paper]() [PDF]()