Deep neural networks may perform poorly when training datasets are heavily
class-imbalanced. Recently, two-stage methods decouple representation learning
and classifier learning to improve performance. But there is still the vital
issue of miscalibration. To address it, we design two methods to improve
calibration and performance in such scenarios. Motivated by the fact that
predicted probability distributions of classes are highly related to the
numbers of class instances, we propose label-aware smoothing to deal with
different degrees of over-confidence for classes and improve classifier
learning. For dataset bias between these two stages due to different samplers,
we further propose shifted batch normalization in the decoupling framework. Our
proposed methods set new records on multiple popular long-tailed recognition
benchmark datasets, including CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT,
Places-LT, and iNaturalist 2018. Code will be available at
this https URL

本文针对训练数据集不均衡和 miscalibration 等问题，设计了两种方法，分别为标签感知平滑和批次偏移规范化，并在多个长尾识别基准数据集上创造了新记录。