在class-balanced loss中,每个类别的损失权重与其样本数量成反比,即样本数量越多的类别,其损失权重越小,样本数量越少的类别,其损失权重越大。这样做的好处是可以更加注重对少数类别的学习,从而提高模型在少数类别上的表现。相比于传统的交叉熵损失函数,class-balanced loss能够更好地处理类别不平衡问题,提高模型在整个...
前两种方法统称 “重采样”(re-sampling),第三种方法称为 “重加权”(re-weighting) 1. 文章1:Class-Balanced Loss 【CVPR2019】【类别不均衡问题】【全监督】:Class-Balanced Loss Based on Effective Number of Samples 核心idea:在类别不平衡的全监督学习任务中,可以使用对不同类损失重加权的...
"Class-Balanced Loss Based on Effective Number of Samples", CVPR2019 2. 论文motivation 对于多分类任务而言,数据集各标签的样本数据有些情况下会呈现长尾分布(dataset with long-tail distribution)。重采样re-sampling会导致出现大量重复样本,不仅会导致训练效率降低,过采样还会导致过拟合。所以本文提出了一种re-...
令pit= sigmoid(zit) = 1/(1 + exp(−zit)),focal loss可表示为: 类平衡(CB)的focal loss为: 最初的focal loss是α-balanced变体。类平衡的focal loss是一样是α-balanced损失,其中αt=(1−β)/(1−βny)。因此,类平衡项可以被视为一个在有效的样本数量的概念基础,明确地在focal loss中设置α...
4. Class-Balanced Loss 上面确定好了样本有效数量N后,接下来就要看怎么将其使用在损失函数中 通过引入一个与样本有效数量成反比的权重因子,设计了类平衡损失来解决从不平衡数据中进行训练的问题。类平衡损失项可应用于大范围的深度网络和损失函数。 对于带有标签labels y∈{1,2,…C}的输入样本x, 其中C是类的总...
To this end, we address the class imbalance problem in the SD domain via a multi-branching (MB) scheme and by weighting the contribution of classes in the overall loss function, resulting in a huge improvement in stuttering classes on the SEP-28 k dataset over the baseline (StutterNet). ...
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Class-Balanced Loss Based on Effective Number of Samples Yin Cui, Menglin Jia,Tsung-Yi Lin,Yang Song,Serge Belongie Dependencies: Python (3.6) Tensorflow (1.14) Datasets: Long-TailedCIFAR. We providea download linkthat includes all the data used in our paper in .tfrecords format. The data ...
[PDF] Class-Balanced Loss Based on Effective Number of Samples | Semantic Scholar2019年的文章,CVPR 2019 刘芷宁:[CVPR 2019] 使用"有效样本数量"来得到类别平衡的损失函数
Our results show that when trained with the proposed class-balanced loss, the network is able to achieve significant performance gains on long-tailed datasets. 展开 关键词: Computer Science - Computer Vision and Pattern Recognition DOI: 10.1109/CVPR.2019.00949 年份: 2019 ...