论文标题
平衡的元符号，用于长尾视觉识别
Balanced Meta-Softmax for Long-Tailed Visual Recognition
论文作者
论文摘要
深层分类器在视觉识别方面取得了巨大的成功。但是，现实世界中的数据长期以来是大自然的，导致培训和测试分布之间的不匹配。在本文中，我们表明，尽管在大多数分类任务中使用了SoftMax功能，但在长尾设置下提供了有偏见的梯度估计。本文介绍了平衡的软马克斯（Softmax），这是软玛克斯（SoftMax）的优雅无偏扩展，以适应训练和测试之间的标签分布变化。从理论上讲，我们得出了多类软性软性回归的概括结合，并显示我们的损失最小化的界限。此外，我们引入了平衡的元符号，应用互补的元抽样器来估计最佳类样本率并进一步改善长尾学习。在我们的实验中，我们证明了在视觉识别和实例分割任务上，平衡的元符号胜过最先进的长尾分类解决方案。
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.
