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...
这篇cvpr2019的论文主要提出了一个损失函数Class-Balanced Loss用来处理数据长尾问题 长尾问题是由于分类问题中数据集每类的数据量不同,导致分类准确度下降。举个极端点的例子有助于理解:A、B二分类问题,数据集中,A、B数据量比例为999:1,为了减少损失值,网络很自然的将所有图片都分到A类,这样准确率为99.9%,但是明...
这篇cvpr2019的论文主要提出了一个损失函数Class-Balanced Loss用来处理数据长尾问题 长尾问题是由于分类问题中数据集每类的数据量不同,导致分类准确度下降。举个极端点的例子有助于理解:A、B二分类问题,数据集中,A、B数据量比例为999:1,为了减少损失值,网络很自然的将所有图片都分到A类,这样准确率为99.9%,但是明...
"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-...
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方法缓解类别不均匀问题。在对各类损失重加权时,通常直接使用样本数量的倒数作为权重,但这...
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...
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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
Meanwhile, to cope with imbalanced DR datasets, we present a class-balanced loss function that performs well in natural image classification tasks, and adopt a simple and easy-to-implement training method for it. The experimental results show that the application of multi-stage transfer and class...