一般的分类任务,即单标签分类,target类别只有1类,都会用softmax+cross_entropy作为loss(pytroch中等价于softmax+log+NLLLoss) 在Jarvix:NLLLoss做了什么中推导了,这个loss= −logsoftmaxtarget ,由于softmax值域为[0,1],log后值域为[ −∞ , 0],再取负数,值域为[0, +∞ ],因此classification loss的值域...
受如何逼近函数的启发-通过泰勒展开,我们提出了一个名为PolyLoss的简单算法来将损失函数设计为多项式函数的线性组合。我们的PolyLoss允许根据目标任务和数据集轻松调整不同多项式基的重要性,同时自然地包含前面提到的ce loss和focal loss。实验结果表明,PolyLoss 中的最优选择确实是取决于任务和数据集。只需引入一个...
IClassificationLoss ILossFunction<TOutput,TLabel> IRegressionLoss IScalarLoss ISupportSdcaClassificationLoss ISupportSdcaLoss ISupportSdcaRegressionLoss ITrainerEstimator<TTransformer,TModel> KMeansModelParameters KMeansTrainer KMeansTrainer.InitializationAlgorithm ...
Syntax L = loss(obj,X,Y) L = loss(obj,X,Y,Name,Value) Description L = loss(obj,X,Y) returns the classification loss, which is a scalar representing how well obj classifies the data in X, when Y contains the true classifications. When computing the loss, loss normalizes the class ...
一、classification 1、应用与问题定义 如果作为regression来处理,为了照顾较远的点会得到紫色的线。因此简单地把归为一个数是不合理的。 比如有3类,分别为123,因为12比较接近,23比较接近,但它们实际并没有关系,就不符合现实情况。 假设是个二分类问题,loss就是错误
classification loss, auxiliary losses such as self-supervised loss27and manifold mixup loss15are also used to provide enough decision boundaries among classes to make the model generalize to new class. These auxiliary losses have been shown to have better pre-training effectiveness in studies such ...
lossClassification loss for discriminant analysis classifier mahalMahalanobis distance to class means of discriminant analysis classifier marginClassification margins for discriminant analysis classifier nLinearCoeffsNumber of nonzero linear coefficients in discriminant analysis classifier ...
focal loss for multi-class classification 转自:https://blog.csdn.net/Umi_you/article/details/80982190 Focal loss 出自何恺明团队Focal Loss for Dense Object Detection一文,用于解决分类问题中数据类别不平衡以及判别难易程度差别的问题。文章中因用于目标检测区分前景和背景的二分类问题,公式以二分类问题为例。
Given an example with a labely=kand a one-hot encoded predictionU(x)=[x_1, x_2, x_3](beingUyour neural network or whatever model you are using), a cost-sensitive loss would be computed by simply taking the scalar product of thek-th row ofMandU(x). As you can see, if your ...
最简单的情况是只考虑一阶多项式,于是有了一些对比实验 但在检测上,有提升的反倒是符号相反的加权 My 2 cents 一种adaptive loss,提供了除改采样率以外的新思路(且两者并不等价),同时还能加速训练收敛,可以考虑