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...
我们设计了一种重加权方案,使用每个类的有效样本数来平衡损失,得到 Class-Balanced Loss 。在人工诱导的长尾CIFAR数据集、包含ImageNet及iNaturalist的大规模数据集上的全面试验表明,在使用 Class-Balanced Loss 进行训练时,使用长尾数据集的网络可以实现显着的性能提升。 1.2 有效样本数(Effective Number of Samples) ...
class balanced learningStuttering is a neuro-developmental speech impairment characterized by uncontrolled utterances (interjections) and core behaviors (blocks, repetitions, and prolongations), and is caused by the failure of speech sensorimotors. Due to its complex nature, stuttering detection (SD) ...
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其中的class_balanced_loss.py: View Code 添加注释和输出的版本: View Code 返回: View Code 可见在代码中能够使用二分类求损失主要是因为将labels转换成了ont-hot格式 labels_one_hot = F.one_hot(labels, no_of_classes).float() 主要比较复杂的就是focal loss的实现: ...
4.3. Class-Balanced Focal Loss 最近提出的focal looss(FL)[26]在sigmoid cross-entropy损失的基础上增加了一个调制因子,以减少分类良好的样本的相对损失,并将重点放在困难样本上。令pit= sigmoid(zit) = 1/(1 + exp(−zit)),focal loss可表示为: ...
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 ...
"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-...
We design a re-weighting scheme that uses the effective number of samples for each class to re-balance the loss, thereby yielding a class-balanced loss. Comprehensive experiments are conducted on artificially induced long-tailed CIFAR datasets and large-scale datasets including ImageNet and ...
[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