Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores

TL;DR

This paper introduces class-wise calibration techniques to improve performance estimation of pre-trained models under domain shifts, especially for imbalanced datasets, enhancing accuracy predictions in classification and segmentation tasks.

Contribution

The paper proposes class-specific modifications of confidence-based evaluation methods to better estimate model performance on imbalanced data under domain shifts.

Findings

Improved accuracy estimation by 18% in classification tasks.

Doubled estimation accuracy in image segmentation.

Effective handling of class imbalance in performance estimation.

Abstract

Machine learning models are typically deployed in a test setting that differs from the training setting, potentially leading to decreased model performance because of domain shift. If we could estimate the performance that a pre-trained model would achieve on data from a specific deployment setting, for example a certain clinic, we could judge whether the model could safely be deployed or if its performance degrades unacceptably on the specific data. Existing approaches estimate this based on the confidence of predictions made on unlabeled test data from the deployment's domain. We find existing methods struggle with data that present class imbalance, because the methods used to calibrate confidence do not account for bias induced by class imbalance, consequently failing to estimate class-wise accuracy. Here, we introduce class-wise calibration within the framework of performance estimation for imbalanced datasets. Specifically, we derive class-specific modifications of state-of-the-art confidence-based model evaluation methods including temperature scaling (TS), difference of confidences (DoC), and average thresholded confidence (ATC). We also extend the methods to estimate Dice similarity coefficient (DSC) in image segmentation. We conduct experiments on four tasks and find the proposed modifications consistently improve the estimation accuracy for imbalanced datasets. Our methods improve accuracy estimation by 18\% in classification under natural domain shifts, and double the estimation accuracy on segmentation tasks, when compared with prior methods.