IoU 的计算公式和这个很像,区别就是 TP 只计算一次: 和Dice soft loss 一样,通过 IoU 计算损失也是使用预测的概率值: 其中C 表示总的类别数。 总结: 交叉熵损失把每个像素都当作一个独立样本进行预测,而 dice loss 和 iou loss 则以一种更“整体”的方式来看待最终的预测输出。 这两类损失是针对不同情况,...
对于每个类别的mask,都计算一个 Dice 损失: 将每个类的 Dice 损失求和取平均,得到最后的 Dice soft loss。 下面是代码实现: def soft_dice_loss(y_true, y_pred, epsilon=1e-6):'''Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions.Assumes the...
这里的γ称作focusing parameter,γ>=0。 称为调制系数(modulating factor) 这里介绍下focal loss的两个重要性质:1、当一个样本被分错的时候,pt是很小的(请结合公式2,比如当y=1时,p要小于0.5才是错分类,难分类样本,此时pt就比较小,反之亦然),因此调制系数就趋于1,也就是说相比原来的loss是没有什么大的改变...
However, this loss function processes all the training data equally. But in our situation, we want to process the data discriminately. For example, we have a csv file corresponding to the training data to indicate the train data is original or augmented. Then we want to define a custom lo...
In this paper, we propose new reinsurance premium principles that minimize the expected weighted loss functions and balance the trade-off between the reinsurer's shortfall risk and the insurer's risk exposure in a reinsurance contract. Random weighting factors are introduced in the weighted loss ...
The weights of the losses due to mean shift and variance shift in the loss function are balanced through a weighting factor lambda, so that the WL chart is considerably more effective than the unadjusted loss function chart, the joint xlowbar &S charts and many other charts as well. The ...
Scheduling Multiclass Packet Streams to Minimize Weighted Loss We consider the problem of scheduling an arriving sequence of packets at a single server. Associated with each packet is a deadline by which the packet mus... RL Givan,EKP Chong,HS Chang - 《Queueing Systems》 被引量: 25发表: ...
We propose a weakly supervised approach to semantic segmentation using bounding box annotations. Bounding boxes are treated as noisy labels for the foreground objects. We predict a per-class attention map that saliently guides the per-pixel cross entropy loss to focus on foreground pixels and refine...
The loss is: qz * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x)) = qz * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x))) = qz * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x))) = qz * log...
1. Weighted Imbalance (Cross-entropoy) Loss Let$\hat{y}$denote the true labels. The weighted imbalance loss for 2-class data can be denoted as: $$ L_{w} = -\sum_{i=1}^m(\alpha\hat{y}_i\log(y_i) + (1-\hat{y}_i)\log(1-y_i)) $$ ...