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By assigning different weights to the authentic and forged classes, Weight-BCELoss ensures that the model pays more attention to the minority class (i.e., forged images) during training. This is important because the number of authentic images usually outweighs the number of forged images in the dataset.
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In both loss functions, the weights play a crucial role in balancing the impact of different classes during the optimization process. By assigning higher weights to less represented classes, the loss functions ensure that the model focuses on detecting and correctly classifying the minority class (forged images) while considering the majority class (authentic images) as well.
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Weight-DiceLoss:
\text{Weight-DiceLoss} = 1 - \frac{{2 \cdot \sum(weight_i \cdot \text{intersection}_i)}}{{\sum(weight_i \cdot \text{union}_i)}}
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Weight-BCELoss:
\text{Weight-BCELoss} = -\sum(weight_i \cdot (y_i \cdot \log(p_i) + (1 - y_i) \cdot \log(1 - p_i)))
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In both formulas, the sum is taken over the two classes (authentic and forged). The weight_i values are the weights assigned to each class, y_i represents the ground truth label (0 for authentic, 1 for forged), and p_i represents the predicted probability.
标签：weight,Weight,cdot,123,forged,images,authentic From： https://www.cnblogs.com/lwp-nicol/p/17444836.html