Abstract
Real-world datasets are inherently class-imbalanced, leading models to favor majority classes and struggle with minority ones. Existing logit adjustment methods address this by modifying logits based on class frequencies, but their class-wise adjustments ignore instance-wise confusion and often suppress informative signals from confusing non-target classes. This results in insufficient learning of such classes, increased prediction uncertainty, and limited generalization. To address this, we propose two complementary methods. First, \textbf{Confusion-based Instance-wise Logit Adjustment (CILA)} adaptively adjusts logits based on instance-wise confusion between the target and non-target classes, restoring informative gradients that class-wise adjustment suppresses. Second, \textbf{Batch-adaptation Noise-Injection (BNI)} perturbs the target logit during training, extending the confusion-aware update to correctly classified but near-boundary samples that the penalty alone cannot reach. CILA is a loss-agnostic plug-in module that can be seamlessly integrated into existing long-tailed learning methods, and consistently improves performance across multiple benchmarks. Our code will be available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27728
