How can you address class imbalance in a dataset when training deep learning models?

Class imbalance can be addressed through various techniques. One common approach is to use resampling methods—either oversampling the minority class or undersampling the majority class. Another effective method is to apply class weights during training, which adjusts the loss function to give more importance to underrepresented classes. Additionally, data augmentation can be applied to increase the diversity of the minority class, while ensemble methods can also be leveraged to improve overall performance on imbalanced datasets.