当有疑问时：使用交替归一化提高分类性能
When in Doubt: Improving Classification Performance with Alternating Normalization
摘要：
我们引入了交替归一化分类（CAN），这是一种用于分类的非参数后处理步骤。CAN通过使用高置信度验证示例的预测类分布重新调整其预测类概率分布来提高具有挑战性的示例的分类准确性。CAN可以很容易地应用于任何概率分类器，具有最小的计算开销。我们使用模拟实验分析CAN的属性，并实证证明其有效性在一组不同的分类任务。
英文摘要：
We introduce Classification with Alternating Normalization (CAN), a non-parametric post-processing step for classification. CAN improves classification accuracy for challenging examples by re-adjusting their predicted class probability distribution using the predicted class distributions of high-confidence validation examples. CAN is easily applicable to any probabilistic classifier, with minimal computation overhead. We analyze the properties of CAN using simulated experiments, and empirically demonstrate its effectiveness across a diverse set of classification tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
