Many real-world recognition problems suffer from an imbalanced or long-tailed label distribution. Those distributions make representation learning more 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. To this aim, recent works have extended softmax cross-entropy using margin modifications, inspired by Bayes' theorem. In this paper, we generalize several approaches with a Balanced Product of Experts (BalPoE), which combines a family of models with different test-time target distributions to tackle the imbalance in the data. The proposed experts are trained in a single stage, either jointly or independently, and fused seamlessly into a BalPoE. We show that BalPoE is Fisher consistent for minimizing the balanced error and perform extensive experiments to validate the effectiveness of our approach. Finally, we investigate the effect of Mixup in this setting, discovering that regularization is a key ingredient for learning calibrated experts. Our experiments show that a regularized BalPoE can perform remarkably well in test accuracy and calibration metrics, leading to state-of-the-art results on CIFAR-100-LT, ImageNet-LT, and iNaturalist-2018 datasets. The code will be made publicly available upon paper acceptance.
翻译:许多现实世界的认知问题都存在不平衡或长期的标签分布问题。这些分布使得代表制学习更具挑战性,因为尾品类的通用性有限。如果测试分布与培训分布不同,例如制服与长尾品不同,则分配转变问题需要解决。为此,最近的工作利用贝耶斯理论的启发,利用差幅修改,扩展了软麦克斯交叉热带植物。在本文中,我们概括了几种具有平衡专家产品(BalPoEE)的方法,这种产品将一系列模型与不同测试时间20的目标分布结合起来,以解决数据不平衡的问题。拟议的专家在单一阶段接受培训,无论是联合还是独立培训,并且无缝地结合到一个BalPoE。我们表明,BalPoE在最大限度地减少平衡错误和进行广泛的实验以验证我们的方法的有效性方面是始终如一的。最后,我们调查了Mixup在这个设置中的影响,发现正规化是学习经校准专家的一个关键成分。我们的实验显示,正常的BalPEE公司可以在测试准确度和校准IFARTRLT结果上对可公开接受的I-FLT。