论文标题
校准专家的均衡产品，用于长尾识别
Balanced Product of Calibrated Experts for Long-Tailed Recognition
论文作者
论文摘要
许多真实的识别问题的特征是长尾标签分布。这些分布使表示形式由于对尾巴类别的概括有限而高度挑战性。如果测试分布不同于训练分布，例如统一与长尾，需要解决分配转移的问题。最近的一项工作建议学习多样化的专家来解决这个问题。各种技术鼓励合奏多样性，例如通过专门为头部和尾部课程中的不同专家。在这项工作中，我们采用了一种分析方法，并将logit调整的概念扩展到合奏，以形成专家的平衡产品（Balpoe）。 Balpoe将一个专家家族与不同的测试时间目标分布相结合，从而推广了几种先前的方法。我们通过证明该合奏是通过Fisher的一致性来最大程度地减少均衡错误，从而展示了如何正确定义这些分布并结合专家以实现无偏见的预测。我们的理论分析表明，我们平衡的合奏需要校准的专家，我们在实践中使用混合实现了校准的专家。我们进行了广泛的实验，我们的方法在三个长尾数据集上获得了新的最新结果：CIFAR-100-LT，Imagenet-LT和Inaturalist-2018。我们的代码可在https://github.com/emasa/balpoe-calibratedlt上找到。
Many real-world recognition problems are characterized by long-tailed label distributions. These distributions make representation learning highly challenging due to limited generalization over the tail classes. If the test distribution differs from the training distribution, e.g. uniform versus long-tailed, the problem of the distribution shift needs to be addressed. A recent line of work proposes learning multiple diverse experts to tackle this issue. Ensemble diversity is encouraged by various techniques, e.g. by specializing different experts in the head and the tail classes. In this work, we take an analytical approach and extend the notion of logit adjustment to ensembles to form a Balanced Product of Experts (BalPoE). BalPoE combines a family of experts with different test-time target distributions, generalizing several previous approaches. We show how to properly define these distributions and combine the experts in order to achieve unbiased predictions, by proving that the ensemble is Fisher-consistent for minimizing the balanced error. Our theoretical analysis shows that our balanced ensemble requires calibrated experts, which we achieve in practice using mixup. We conduct extensive experiments and our method obtains new state-of-the-art results on three long-tailed datasets: CIFAR-100-LT, ImageNet-LT, and iNaturalist-2018. Our code is available at https://github.com/emasa/BalPoE-CalibratedLT.
