Long-Tailed Recognition via Information-Preservable Two-Stage Learning

• URL: http://arxiv.org/abs/2510.08836v1
• Date: Thu, 09 Oct 2025 21:49:12 GMT
• Title: Long-Tailed Recognition via Information-Preservable Two-Stage Learning
• Authors: Fudong Lin, Xu Yuan,
• Abstract summary: The imbalance (or long-tail) is the nature of many real-world data distributions.<n>We propose a novel two-stage learning approach to mitigate such a majority-biased tendency.<n>Our approach achieves the state-of-the-art performance across various long-tailed benchmark datasets.
• Score: 6.2471093754692815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The imbalance (or long-tail) is the nature of many real-world data distributions, which often induces the undesirable bias of deep classification models toward frequent classes, resulting in poor performance for tail classes. In this paper, we propose a novel two-stage learning approach to mitigate such a majority-biased tendency while preserving valuable information within datasets. Specifically, the first stage proposes a new representation learning technique from the information theory perspective. This approach is theoretically equivalent to minimizing intra-class distance, yielding an effective and well-separated feature space. The second stage develops a novel sampling strategy that selects mathematically informative instances, able to rectify majority-biased decision boundaries without compromising a model's overall performance. As a result, our approach achieves the state-of-the-art performance across various long-tailed benchmark datasets, validated via extensive experiments. Our code is available at https://github.com/fudong03/BNS_IPDPP.

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