Cross-Entropy Loss

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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. [https://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