focal loss通过增加难分样本的权重并减小易分样本权重,从而缓解类别不平衡现象带来的忽视正类现象 参考 [1] Use Focal Loss To Train Model Using Imbalanced Dataset https://leimao.github.io/blog/Focal-Loss-Explained/ [2] Review: RetinaNet — Focal Loss (Object Detection) https://towardsdatascience.co...
For image classification specific, data augmentation techniques are also variable to create synthetic data for under-represented classes. The focal loss is designed to address class imbalance by down-weighting inliers (easy examples) such that their contribution to the total loss is small even if the...
英文原文:Multi-class classification with focal loss for imbalanced datasets 标签:深度学习 01 Focus on hard examples The focal loss was proposed for dense object detection task early this year. It enables training highly accurate dense object detectors with an imbalance between foreground and background...
Dice Loss 来自《Dice Loss for Data-imbalanced NLP Tasks》这篇论文,阐述在 NLP 的场景中,这种类别数据不均衡的问题也是十分常见,比如机器阅读理解machine reading comprehension(MRC),与上述论文表明的观点大致相同: 负样本数量远超过正样本,导致容易的负样本会主导了模型的训练; 另外,还指出交叉熵其实是准确率(acc...
2.2 GHM Loss 上面的 Focal Loss 注重了对 hard example 的学习,但不是所有的 hard example 都值得关注,有一些 hard example 很可能是离群点,这种离群点当然是不应该让模型关注的。 GHM (gradient harmonizing mechanism) 是一种梯度调和机制,GHM Loss 的改进思想有两点:1)就是在使模型继续保持对 hard example...
Dice Loss 是来自文章 V-Net 提出的,DSC Loss 是香侬科技的 Dice Loss for Data-imbalanced NLP Tasks。 按照上面的逻辑,看一下 Dice Loss 是怎么演变过来的。Dice Loss 主要来自于 dice coefficient,dice coefficient 是一种用于评估两个样本的相似性的度量函数。
2.3 Dice Loss & DSC Loss Dice Loss 是来自文章 V-Net 提出的,DSC Loss 是香侬科技的 Dice Loss for Data-imbalanced NLP Tasks。 按照上面的逻辑,看一下 Dice Loss 是怎么演变过来的。Dice Loss 主要来自于 dice coefficient,dice coefficient 是一种用于评估两个样本的相似性的度量函数。
To tackle the class imbalance, a loss function called Focal Loss (FL) is explored. Results suggests that when all the classes are equally important, the FL does not improve the classification performance. However, if the objective is to detect the minority class, the FL achieves the best ...
6.Focal loss指出，为什么要用balanced的交叉熵去学习imbalanced的类别呢？真牛逼。于是，将predictions作为...
driven的方式来学习data的权重？这样可以统一起OHEM和self paced learning这两个极端。Focal loss其实也...