Accurate Layerwise Interpretable Competence Estimation
This addresses the unsolved issue of predicting model competence for classification tasks, which is crucial for reliable deployment in applications like autonomous systems or medical diagnosis, though it appears incremental as it builds on existing confidence estimation methods.
The paper tackles the problem of estimating machine learning model performance in real-world scenarios by introducing a statistically rigorous definition of competence and the ALICE Score, which shows significant improvements over state-of-the-art confidence estimators on datasets like DIGITS, CIFAR10, and CIFAR100.
Estimating machine learning performance 'in the wild' is both an important and unsolved problem. In this paper, we seek to examine, understand, and predict the pointwise competence of classification models. Our contributions are twofold: First, we establish a statistically rigorous definition of competence that generalizes the common notion of classifier confidence; second, we present the ALICE (Accurate Layerwise Interpretable Competence Estimation) Score, a pointwise competence estimator for any classifier. By considering distributional, data, and model uncertainty, ALICE empirically shows accurate competence estimation in common failure situations such as class-imbalanced datasets, out-of-distribution datasets, and poorly trained models. Our contributions allow us to accurately predict the competence of any classification model given any input and error function. We compare our score with state-of-the-art confidence estimators such as model confidence and Trust Score, and show significant improvements in competence prediction over these methods on datasets such as DIGITS, CIFAR10, and CIFAR100.