Abstract
Class imbalance induces systematic bias in deep neural networks by imposing a skewed effective class prior. This work introduces the Neural Prior Estimator (NPE), a framework that learns feature-conditioned log-prior estimates from latent representations. NPE employs one or more Prior Estimation Modules trained jointly with the backbone via a one-way logistic loss. Under the Neural Collapse regime, NPE is analytically shown to recover the class log-prior up to an additive constant, providing a theoretically grounded adaptive signal without requiring explicit class counts or distribution-specific hyperparameters. The learned estimate is incorporated into logit adjustment, forming NPE-LA, a principled mechanism for bias-aware prediction. Experiments on long-tailed CIFAR and imbalanced semantic segmentation benchmarks (STARE, ADE20K) demonstrate consistent improvements, particularly for underrepresented classes. NPE thus offers a lightweight and theoretically justified approach to learned prior estimation and imbalance-aware prediction.
Abstract (translated)
类不平衡会在深度神经网络中引入系统偏差，通过施加扭曲的效用类别先验。这项工作介绍了神经优先估计器（NPE），这是一种框架，能够从潜在表示中学习到特征条件下的对数先验估计值。NPE 使用一个或多个与主干模型共同训练的优先估计模块，并采用单向逻辑损失函数。在神经崩溃制度下，NPE 被理论证明可以恢复类别对数先验（除了一个可加常量），从而提供一种无需显式类计数或特定分布超参数的自适应信号。所学得的估计值被整合进 logits 调整中，形成 NPE-LA，这是一种偏见感知预测的原则机制。在长尾 CIFAR 和不平衡语义分割基准测试（如 STARE、ADE20K）上的实验显示了对代表性不足类别的持续改进效果。因此，NPE 提供了一种轻量级且理论基础坚实的类先验估计和偏差感知预测方法。
URL
https://arxiv.org/abs/2602.17853
