论文标题
长尾数据集中多标签分类的分配均衡损失
Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
论文作者
论文摘要
我们提出了一种新的损失函数，称为长尾分布的多标签识别问题，称为分配平衡损失。与常规的单标签分类问题相比，由于两个重大问题，即标签的共发生和负面标签的优势（当视为多个二元分类问题时），多标签识别问题通常更具挑战性。分配平衡的损失通过对标准二进制二进制跨循环损失的两个关键修改解决了这些问题：1）一种重新平衡权重的新方法，考虑到标签共发生引起的影响，以及2）负耐受的正则化，以减轻负面标签的过度抑制。对Pascal VOC和可可的实验表明，使用这种新损失功能训练的模型可在现有方法上获得显着的性能提高。代码和型号可在以下网址提供：https：//github.com/wutong16/distributionbalancedloss。
We present a new loss function called Distribution-Balanced Loss for the multi-label recognition problems that exhibit long-tailed class distributions. Compared to conventional single-label classification problem, multi-label recognition problems are often more challenging due to two significant issues, namely the co-occurrence of labels and the dominance of negative labels (when treated as multiple binary classification problems). The Distribution-Balanced Loss tackles these issues through two key modifications to the standard binary cross-entropy loss: 1) a new way to re-balance the weights that takes into account the impact caused by label co-occurrence, and 2) a negative tolerant regularization to mitigate the over-suppression of negative labels. Experiments on both Pascal VOC and COCO show that the models trained with this new loss function achieve significant performance gains over existing methods. Code and models are available at: https://github.com/wutong16/DistributionBalancedLoss .
