Frequency-Aware Self-Supervised Long-Tailed Learning

• URL: http://arxiv.org/abs/2309.04723v2
• Date: Fri, 15 Sep 2023 12:46:57 GMT
• Title: Frequency-Aware Self-Supervised Long-Tailed Learning
• Authors: Ci-Siang Lin, Min-Hung Chen, Yu-Chiang Frank Wang
• Abstract summary: We propose Frequency-Aware Self-Supervised Learning (FASSL) for learning from unlabeled data with inherent long-tailed distributions. 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 exploited.
• Score: 36.00672675332761
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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