Why Not Hyperparameter-Friendly Optimisation? A Monotonic Adaptive Norm Rescaling Approach For Long-Tailed Recognition

• URL: http://arxiv.org/abs/2606.02526v1
• Date: Mon, 01 Jun 2026 17:34:38 GMT
• Title: Why Not Hyperparameter-Friendly Optimisation? A Monotonic Adaptive Norm Rescaling Approach For Long-Tailed Recognition
• Abstract summary: Long-tailed recognition is a significant challenge for deep learning.<n>In this paper, we provide a class-conditional distribution perspective to support norm rescaling methods.<n>We propose a simple but effective approach called Self-Adaptive Monotonic Normalization (SAMN)<n>SAMN directly enforces monotonicity on per-class weight norms using the Pool Adjacent Violators Algorithm.
• Score: 7.863135301818823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-tailed recognition poses a significant challenge for deep learning. The two-stage decoupling paradigm, which separates representation learning from classifier retraining, offers a promising solution. During the classifier retraining stage, adaptive norm rescaling is a popular technique. It adjusts the per-class weight norms via parameter regularization, which inevitably introduces hyperparameters. However, many studies report that long-tailed recognition is sensitive to these hyperparameters, as their setup significantly impacts performance. In this paper, we first provide a class-conditional distribution perspective to support norm rescaling methods. Furthermore, we propose a simple but effective approach called Self-Adaptive Monotonic Normalization (SAMN). SAMN avoids the need for parameter regularization. It directly enforces monotonicity on per-class weight norms using the Pool Adjacent Violators Algorithm, making the method hyperparameter-friendly. SAMN is a universal strategy that integrates seamlessly with other methods for enhanced performance. Experiments on benchmark datasets demonstrate that our method significantly boosts long-tailed recognition performance, often achieving state-of-the-art results.

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