Minority class incremental rectification
Minority class incremental rectification is a deep learning technique that boosts the representation of minority classes in imbalanced datasets using a Class Rectification Loss (CRL). This strategy dynamically adjusts to class imbalance, enhancing model performance by incorporating hard example mining and other methods.
This technique is based on the paper by Dong et al. [5][6]. Here are the main steps of the technique:
- Class identification in each batch:
- Binary classification: We consider a class as a minority if it makes up less than 50% of the batch. The rest is the majority class.
- Multi-class classification: We define all minority classes as those that collectively account for no more than 50% of the batch. The remaining classes are treated as majority classes.
- Compute the class rectification loss:
- Locate challenging samples:
- Find hard positives: We identify samples from the minority class that our model incorrectly assesses with...
- Locate challenging samples:
