AI 中文总结
针对长尾分类中现有方法依赖静态频率忽略学习动态的问题，提出学习动态感知损失函数LDAL，利用特征表示强度、类学习难度及预测变化调整权重，实验证明该方法显著超越现有方法，平衡了准确性和泛化性。
AI 中文摘要
计算机视觉中的深度学习模型在长尾数据集上训练时面临重大挑战，少数多数类主导，多数少数类严重代表性不足。现有重加权方法依赖静态类频率惩罚模型，忽略网络随时间学习类的动态特性。我们引入新颖的学习动态感知损失（LDAL）函数解决此问题，它将重点从静态样本计数转移到动态学习进度。LDAL框架通过利用：（i）学习到的特征表示强度（语义尺度），（ii）通过预测的香农熵测量的每个类的内在学习难度，以及（iii）跟踪连续epoch之间预测变化以稳定训练并避免局部最小值的epoch间正则化项，持续调整类权重。LDAL纯粹是一个目标函数，在适应模型特征学习时产生可忽略的计算开销。在多个基准数据集上的实验结果表明，我们的方法显著超越了现有的重加权损失函数，在准确性和泛化性之间提供了最佳平衡。源代码可在此https URL获得
英文摘要
Deep learning models in computer vision face significant challenges when trained on long-tailed datasets, where a few majority classes dominate while many minority classes are severely underrepresented. Such imbalances frequently arise in real-world scenarios such as rare species recognition, manufacturing fault detection, and medical image understanding, leading to biased models that underperform on tail classes. Existing reweighting methods typically rely on static class frequencies to penalize the model, ignoring the dynamic nature of how effectively a network actually learns a class over time. We address this by introducing a novel Learning-Dynamics Aware Loss (LDAL) function that shifts the focus from static sample counts to dynamic learning progress. LDAL framework adjusts class weights continuously by leveraging: (i) the strength of learned feature representations (semantic scale), (ii) the intrinsic learning difficulty of each class, measured via the Shannon entropy of its predictions, and (iii) an inter-epoch regularizer term that tracks prediction shifts between consecutive epochs to stabilize training and avoid local minima. LDAL is purely a objective function which incurs negligible computational overhead while adapting to the feature learning of the model. Experimental results on multiple benchmark datasets demonstrate that our approach significantly surpasses state-of-the-art reweighting loss functions, providing an optimal trade-off between accuracy and generalizability. The source code is available at https://github.com/sdm2026/ldal
