Difference between revisions of "Cross-Entropy Loss"
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* [[hyperparameter]] | * [[hyperparameter]] | ||
| − | Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. [http://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html | + | Cross-entropy [[loss]], or log [[loss]], measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. [http://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html |
Cross-entropy loss is one of the most widely used loss functions in classification scenarios. In face recognition tasks, the cross-entropy loss is an effective method to eliminate outliers. [http://arxiv.org/pdf/1904.09523.pdf Neural Architecture Search for Deep Face Recognition | Ning Zhu] | Cross-entropy loss is one of the most widely used loss functions in classification scenarios. In face recognition tasks, the cross-entropy loss is an effective method to eliminate outliers. [http://arxiv.org/pdf/1904.09523.pdf Neural Architecture Search for Deep Face Recognition | Ning Zhu] | ||
http://ml-cheatsheet.readthedocs.io/en/latest/_images/cross_entropy.png | http://ml-cheatsheet.readthedocs.io/en/latest/_images/cross_entropy.png | ||
Revision as of 20:07, 9 April 2020
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Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. [http://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
Cross-entropy loss is one of the most widely used loss functions in classification scenarios. In face recognition tasks, the cross-entropy loss is an effective method to eliminate outliers. Neural Architecture Search for Deep Face Recognition | Ning Zhu
