To better inform the calibrator about the correctness of each prediction beyond using transformed images, the following techniques could be explored:
Feature Engineering: Instead of relying solely on image transformations, extracting and incorporating additional features from the data, such as texture, shape, or context information, could provide more diverse and informative inputs for the calibrator.
Meta-Learning: Implementing meta-learning techniques to adapt the calibrator's learning process to different prediction correctness scenarios could enhance its ability to distinguish between correct and incorrect predictions more effectively.
Active Learning: Incorporating active learning strategies to selectively choose informative samples for calibrator training based on their prediction correctness could improve the model's performance on in-distribution test sets.
Self-Supervised Learning: Leveraging self-supervised learning methods to pre-train the calibrator on auxiliary tasks related to prediction correctness could help the model learn more robust representations for calibration.