To further improve the performance of the Correctness-Aware (CA) loss, especially for in-distribution test sets where it is less advantageous compared to state-of-the-art methods, several strategies can be considered:
Improved Correctness Prediction: Enhancing the method used to determine the correctness of each prediction can lead to better calibration. Exploring more sophisticated techniques, such as leveraging uncertainty estimation methods like Monte Carlo Dropout or Bayesian Neural Networks, could provide more reliable signals for correctness awareness.
Data Augmentation: Experimenting with a wider range of data augmentation techniques beyond the ones mentioned in the paper, such as random cropping, translation, or adding noise, could potentially provide more diverse and informative transformed images for calibrator training.
Model Architecture: Adapting the architecture of the calibrator network, such as increasing its complexity or incorporating attention mechanisms, could help capture more nuanced features from the transformed images and improve the calibration performance.
Ensemble Methods: Utilizing ensemble methods by combining multiple calibrators trained on different subsets of transformed images or with different hyperparameters could enhance the robustness and generalization of the calibration model.
Fine-tuning Hyperparameters: Fine-tuning hyperparameters of the calibrator, such as learning rate, batch size, or regularization techniques, could optimize the training process and improve the calibration performance on in-distribution test sets.