Adaptive Paradigm Synergy: Can a Cross-Paradigm Objective Enhance Long-Tailed Learning?

• URL: http://arxiv.org/abs/2410.22883v1
• Date: Wed, 30 Oct 2024 10:25:22 GMT
• Title: Adaptive Paradigm Synergy: Can a Cross-Paradigm Objective Enhance Long-Tailed Learning?
• Authors: Haowen Xiao, Guanghui Liu, Xinyi Gao, Yang Li, Fengmao Lv, Jielei Chu,
• Abstract summary: Self-supervised learning (SSL) has achieved impressive results across several computer vision tasks, even rivaling supervised methods. However, its performance degrades on real-world datasets with long-tailed distributions due to difficulties in capturing inherent class imbalances. We introduce Adaptive Paradigm Synergy (APS), a cross-paradigm objective that seeks to unify the strengths of both paradigms.
• Score: 16.110763554788445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised learning (SSL) has achieved impressive results across several computer vision tasks, even rivaling supervised methods. However, its performance degrades on real-world datasets with long-tailed distributions due to difficulties in capturing inherent class imbalances. Although supervised long-tailed learning offers significant insights, the absence of labels in SSL prevents direct transfer of these strategies.To bridge this gap, we introduce Adaptive Paradigm Synergy (APS), a cross-paradigm objective that seeks to unify the strengths of both paradigms. Our approach reexamines contrastive learning from a spatial structure perspective, dynamically adjusting the uniformity of latent space structure through adaptive temperature tuning. Furthermore, we draw on a re-weighting strategy from supervised learning to compensate for the shortcomings of temperature adjustment in explicit quantity perception.Extensive experiments on commonly used long-tailed datasets demonstrate that APS improves performance effectively and efficiently. Our findings reveal the potential for deeper integration between supervised and self-supervised learning, paving the way for robust models that handle real-world class imbalance.

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