class-balanced loss

**Class-Balanced Loss** is a **loss function modification that re-weights the loss for each class based on the effective number of samples** — addressing class imbalance by assigning higher weight to under-represented classes, preventing the model from being dominated by majority classes. **Class-Balanced Loss Formulation** - **Effective Number**: $E_n = frac{1 - eta^n}{1 - eta}$ where $n$ is the number of samples and $eta in [0,1)$ is the overlap parameter. - **Weight**: $w_c = frac{1}{E_{n_c}}$ — inversely proportional to the effective number of samples in class $c$. - **Loss**: $L_{CB} = frac{1}{E_{n_c}} L(x, y)$ — applies the weight to the standard loss (cross-entropy, focal loss, etc.). - **$eta$ Parameter**: $eta = 0$ gives uniform weights; $eta ightarrow 1$ gives inverse-frequency weights. **Why It Matters** - **Long-Tail**: Many real-world datasets follow a long-tail distribution — few dominant classes, many rare classes. - **Semiconductor**: Defect types follow a long-tail distribution — common defects dominate rare but critical ones. - **Effective Number**: Accounts for data overlap — more sophisticated than simple inverse-frequency weighting. **Class-Balanced Loss** is **weighing by rarity** — giving more importance to under-represented classes based on their effective sample count.