The segmentation performance may be improved by addressing the class
imbalance implicit in the task. In this sense, one idea could be exploring
alternative losses such as the weighted cross-entropy loss, the focal loss, and
the class-balanced loss.
Weighted Cross-Entropy Loss: In the context of segmentation, this involves assigning different weights to the different classes in the cross-entropy loss calculation. The idea is to give more importance (higher weight) to the minority classes, thereby encouraging the model to pay more attention to them during training.
Focal Loss: The focal loss is designed to address class imbalance and the problem of easy-to-classify samples dominating the learning process. It does this by down-weighing the loss for well-classified examples. In the context of segmentation, the focal loss would penalize confident predictions less, which can help with better handling of minority classes.
Class Balanced Loss: This is a loss function specifically designed to address class imbalance. It dynamically adjusts the contribution of each class to the overall loss based on its frequency in the dataset. It effectively gives more weight to the minority classes, ensuring they are not ignored during training.
The segmentation performance may be improved by addressing the class imbalance implicit in the task. In this sense, one idea could be exploring alternative losses such as the weighted cross-entropy loss, the focal loss, and the class-balanced loss.