On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning.

Jianhong Bai, Zuozhu Liu, Hualiang Wang, Jin Hao, YANG FENG, Huanpeng Chu, Haoji Hu

Keywords: self-supervised learning, long-tail learning, out-of-distribution data

Abstract: Though Self-supervised learning (SSL) has been widely studied as a promising technique for representation learning, it doesn't generalize well on long-tailed datasets due to the majority classes dominating the feature space. Recent work shows that the long-tailed learning performance could be boosted by sampling extra in-domain (ID) data for self-supervised training, however, large-scale ID data which can rebalance the minority classes are expensive to collect. In this paper, we propose an alternative but easy-to-use and effective solution, \textbf{C}ontrastive with \textbf{O}ut-of-distribution (OOD) data for \textbf{L}ong-\textbf{T}ail learning (COLT), which can effectively exploit OOD data to dynamically re-balance the feature space. We empirically identify the counter-intuitive usefulness of OOD samples in SSL long-tailed learning and principally design a novel SSL method. Concretely, we first localize the `\emph{head}' and `\emph{tail}' samples by assigning a tailness score to each OOD sample based on its neighborhoods in the feature space. Then, we propose an online OOD sampling strategy to dynamically re-balance the feature space. Finally, we enforce the model to be capable of distinguishing ID and OOD samples by a distribution-level supervised contrastive loss. Extensive experiments are conducted on various datasets and several state-of-the-art SSL frameworks to verify the effectiveness of the proposed method. The results show that our method significantly improves the performance of SSL on long-tailed datasets by a large margin, and even outperforms previous work which uses external ID data. Our code is available at \url{https://github.com/JianhongBai/COLT}.

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