asymmetric loss functions

**Asymmetric Loss Functions** are **loss functions that apply different penalties for positive vs. negative class errors** — designed for imbalanced datasets or situations where false positives and false negatives have unequal costs, treating each type of mistake differently. **Asymmetric Loss Designs** - **Asymmetric Focal Loss**: Down-weight easy negatives MORE than easy positives to handle extreme imbalance. - **Weighted BCE**: $L = -[alpha y log(hat{y}) + (1-alpha)(1-y)log(1-hat{y})]$ — $alpha$ controls positive vs. negative weight. - **Asymmetric Softmax**: Apply different temperatures/thresholds for positive and negative classes. - **Hard-Threshold**: Ignore negative samples with very low probability — focus only on informative negatives. **Why It Matters** - **Multi-Label**: In multi-label classification, negative labels vastly outnumber positive — asymmetric loss handles this. - **Extreme Imbalance**: When positive:negative ratio is 1:1000+, asymmetric treatment is essential. - **Semiconductor**: Defect detection with rare positive cases (defects) among vast negative cases (good wafers). **Asymmetric Loss** is **punishing mistakes unequally** — applying different penalties for positive and negative errors to handle real-world cost asymmetry.