The choice of the loss function is critical in extreme multi-label learning where the objective is to annotate each data point with the most relevant {it subset} of labels from an extremely large label set.
首先对于第一点，node本身的特征，在不同任务中，node本身特征具有不同的表示，比如，对于我们的Label G...
现在遇到 MLL task, 第一个想到的就是 DNN + binary cross entropy loss.这
[CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural networks这篇论文里设计了针对多标签问题的loss,传统的类别分类不适用,作者把这个任务视为多个二分类问题,loss function和分类的分数如下:
The choice of the loss function is critical in extreme multi-label learning where the objective is to annotate each data point with the most relevant subset of labels from an extremely large label set. Unfortunately, existing loss functions, such as the Hamming loss, are unsuitable for learning...
Loss functionNon-additive measuresAnalysisLabel dependenceThe idea to exploit label dependencies for better prediction is at the core of methods for multi-label classification (MLC), and performance improvements are normally explained in this way. Surprisingly, however, there is no established methodology...
Following the idea here,#2826, I also give a try to categorial_crossentropy but still have no such luck. Any tips on choosing the loss function for multi-label classification task is beyond welcome. Thanks in advance.
Given an observed dataset {(xn, yn)}Nn=1, we can use stan- dard techniques to approximately solve θˆfull = argminθ 1 N N L(f (xn; θ), yn) n=1 (2) where L : [0, 1]L × Y → R is a suitable multi-label loss function e.g. binary cross-entropy or softmax cro...
metrics=[{'name':'accuracy','function':accuracy_multilabel}] 因为是多标签分类,所以我们用的是准确率衡量指标是accuracy_multilabel。 我们把当前的参数设置,存入到日志记录器中。 代码语言:javascript 复制 logger.info(args) 开始构造模型了。 代码语言:javascript ...
metrics = [{'name': 'accuracy', 'function': accuracy_multilabel}] 因为是多标签分类,所以我们用的是准确率衡量指标是accuracy_multilabel。 我们把当前的参数设置,存入到日志记录器中。 logger.info(args) 开始构造模型了。 learner = BertLearner.from_pretrained_model(databunch, BERT_PRETRAINED_MODEL, met...