A deep learning model for image recognition is misclassifying specific minority classes at a substantially higher rate than majority classes. How would you address this imbalance and improve classification performance?

Addressing class imbalance in deep learning models often involves rebalancing the dataset through techniques like oversampling or undersampling. This ensures that minority classes receive adequate attention during training and can improve classification performance.