Long-tailed classification poses a challenge due to its heavy imbalance in class probabilities and tail-sensitivity risks with asymmetric misprediction costs. Recent attempts have used re-balancing loss and ensemble methods, but they are largely heuristic and depend heavily on empirical results, lacking theoretical explanation. Furthermore, existing methods overlook the decision loss, which characterizes different costs associated with tailed classes. This paper presents a general and principled framework from a Bayesian-decision-theory perspective, which unifies existing techniques including re-balancing and ensemble methods, and provides theoretical justifications for their effectiveness. From this perspective, we derive a novel objective based on the integrated risk and a Bayesian deep-ensemble approach to improve the accuracy of all classes, especially the "tail". Besides, our framework allows for task-adaptive decision loss which provides provably optimal decisions in varying task scenarios, along with the capability to quantify uncertainty. Finally, We conduct comprehensive experiments, including standard classification, tail-sensitive classification with a new False Head Rate metric, calibration, and ablation studies. Our framework significantly improves the current SOTA even on large-scale real-world datasets like ImageNet.

翻译：长尾分类由于类别概率分布严重不平衡和灾难性尾部风险造成挑战。近期研究使用再平衡损失和集成方法尝试解决问题，但这些方法很大程度上基于经验性结果，缺乏理论解释。此外，现有方法忽视了决策损失，该损失描述了具有不同代价的尾类别的判定代价。本文从贝叶斯决策论角度提出了一个通用而有原则的框架，将再平衡和集成等现有技术统一起来，并为它们的有效性提供了理论证明。从这个角度出发，我们提出了一个基于综合风险和贝叶斯深度集成方法的新目标，以提高所有类别的准确性，特别是"尾部"类别的准确性。此外，我们的框架允许任务自适应决策损失，提供变化任务场景中可证明最优决策的能力，并具有量化不确定性的能力。最后，我们进行了全面的实验，包括标准分类、新的错误头部率度量的灾难性尾部分类、校准和消融研究。我们的框架即使在像ImageNet这样的大规模真实世界数据集上也显着提高了当前SOTA的水平。