本文为 AI 研习社编译的技术博客,原标题 : Multi-class classification with focal loss for imbalanced datasets 作者 |Chengwei Zhang翻译 | 汪鹏 校对 | 斯蒂芬·二狗子 审核 | Pita 整理 | 立鱼王 原文链接: https://medium.com/swlh/multi-class-classification-with-focal-loss-for-imbalanced-datasets-c47...
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...
Class imbalanced dataConditional Variational Auto-Encoder (CVAE)Focal lossThe distribution of the health data monitored from mechanical system in the industries is class imbalanced mainly. The amount of monitoring data for the normal condition is far more than the monitoring data for different fault ...
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...
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; ...
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。
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...
An implementation of the focal loss to be used with LightGBM for binary and multi-class classification problems python3lightgbmimbalanced-datafocal-loss UpdatedNov 9, 2019 Python prstrive/UniMVSNet Star237 [CVPR 2022] Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation ...
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。
Focal Loss的引入主要是为了解决难易样本数量不平衡(注意,有区别于正负样本数量不平衡)的问题,实际可以使用的范围非常广泛。 该损失函数来源于论文Focal Loss for Dense Object Detection,作者利用它改善了图像物体检测的效果。不过Focal Loss完全是一个通用性的Loss,因为在 NLP中,也存在大量的类别不平衡的任务。