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?

  • Adjust the learning rate.
  • Rebalance the dataset through oversampling or undersampling.
  • Increase the model's complexity.
  • Use a different activation function.
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.
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