使用ResNet-32 作为框架在 Imbalanced CIFAR-10 数据集上的实验结果,测试集样本量均为 1000 在Imbalanced CIFAR-10 和 CIFAR-100 数据集上的实验结果 论文信息 Influence-Balanced Loss for Imbalanced Visual Classification
I need to train a multi-label classifier for text topic classification task. Having searched around the internet, I follow the suggestion to use sigmoid + binary_crossentropy. But I can't get good results (i.e. subset accuracy) on the va...
In this paper, we present an asymmetric stagewise least square (ASLS) loss function for imbalanced classification. While keeping all the advantages of the stagewise least square (SLS) loss function, such as, better robustness, computational efficiency and sparseness, the ASLS loss extends the SLS...
Based on minimizing misclassification cost, a new robust loss function is designed in this paper to deal with the imbalanced classification problem under noise environment. It is nonconvex but maintains Fisher consistency. Applying the proposed loss function into support vector machine (SVM), a ...
Loss会直接体现在FC层之前的输出上。进一步地，IB Loss引入了类别数量的平衡，即样本多的类别权重减小，确保所有类别在模型决策中均衡发挥作用。这种方法直观易懂，论文《Influence-Balanced Loss for Imbalanced Visual Classification》提供了详细的实现细节和实验结果，可参考arxiv.org/pdf/2110.0244...
Paper:https://arxiv.org/abs/2110.02444 Code:https://github.com/pseulki/IB-Loss visual representation研究动机 挑战:许多现实世界的数据表现出偏态分布,其中每个类别的样本数量差异很大。类别之间的这种…
In other words, the focal loss function truly enabled the CNNs models to be less biased towards the majority class than the cross-entropy did in the classification task of imbalanced dog red blood cell data.This is a preview of subscription content, log in via an institution to check access...
论文:AM-LFS:AutoML for Loss Function Search不过这篇文章将介绍一下如何使用AutoML技术来搜索损失函数。一般来说,损失函数都是需要我们手动设计的,以分类任务而言,我们通常会使用交叉熵。碰到数据集imbalanced的情况,可能会给每个类别加上一个权重。在RetinaNet论文里为目标检测任务提出了FocalLoss。上述都是对交叉熵函...
For more details on loss functions, see Classification Loss. Example: LossFun="binodeviance" Example: LossFun=@Lossfun Data Types: char | string | function_handle Mode— Aggregation level for output "ensemble" (default) | "individual" | "cumulative" Aggregation level for the output, specified...
AM-LFS:AutoML for Loss Function Search 【AutoML:Survey of the State-of-the-Art】。 论文:AM-LFS:AutoML for Loss Function Search 不过这篇文章将介绍一下如何使用AutoML技术来搜索损失函数。一般来说,损失函数都是需要我们手动设计的,以分类任务而言,我们通常会使用交叉熵。碰到数据集imbalanced的情况,可能会...