Deep classifiers have achieved great success in visual recognition. However,
real-world data is long-tailed by nature, leading to the mismatch between
training and testing distributions. In this paper, we show that the Softmax
function, though used in most classification tasks, gives a biased gradient
estimation under the long-tailed setup. This paper presents Balanced Softmax,
an elegant unbiased extension of Softmax, to accommodate the label distribution
shift between training and testing. Theoretically, we derive the generalization
bound for multiclass Softmax regression and show our loss minimizes the bound.
In addition, we introduce Balanced Meta-Softmax, applying a complementary Meta
Sampler to estimate the optimal class sample rate and further improve
long-tailed learning. In our experiments, we demonstrate that Balanced
Meta-Softmax outperforms state-of-the-art long-tailed classification solutions
on both visual recognition and instance segmentation tasks.

本文提出了无偏的 Balanced Softmax 方法和 Balanced Meta-Softmax 方法，以解决训练和测试分布不匹配的深度学习分类问题，实验证明其在视觉识别和实例分割任务中胜于现有最佳方法。