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...
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...
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...
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αt的方式。 其实上面三个损失的CB版本就是在原来的式子中增加了一个特定的权重weight 实现可见Class-Balanced Loss Based on Effective Number of Samples - 2 - 代码学习...
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 ...
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class ClassBalancedCrossEntropyLoss(nn.Module): def __init__(self, weights=None, ignore_index=-100): super(ClassBalancedCrossEntropyLoss, self).__init__() self.weights = weights self.ignore_index = ignore_index def forward(self, inputs, targets): # inputs: (batch_size, num_classes) ...
"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-...
Scene text detectionClass imbalanceGradient imbalanceTo address class imbalance issue in scene text detection, we propose two novel loss functions, namely Class-Balanced Self Adaption Loss (CBSAL) and Class-Balanced First Power Loss (CBFPL)...doi...
[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