Improving Classifier Confidence using Lossy Label-Invariant Transformations
This addresses the need for reliable uncertainty estimates in autonomous agents and humans, but it is incremental as it builds on existing manifold-based calibration techniques.
The paper tackles the problem of poor confidence calibration in trained models by introducing ReCal, a technique that uses lossy label-invariant transformations to group and calibrate inputs without retraining, showing it outperforms other methods on datasets like ImageNet.
Providing reliable model uncertainty estimates is imperative to enabling robust decision making by autonomous agents and humans alike. While recently there have been significant advances in confidence calibration for trained models, examples with poor calibration persist in most calibrated models. Consequently, multiple techniques have been proposed that leverage label-invariant transformations of the input (i.e., an input manifold) to improve worst-case confidence calibration. However, manifold-based confidence calibration techniques generally do not scale and/or require expensive retraining when applied to models with large input spaces (e.g., ImageNet). In this paper, we present the recursive lossy label-invariant calibration (ReCal) technique that leverages label-invariant transformations of the input that induce a loss of discriminatory information to recursively group (and calibrate) inputs - without requiring model retraining. We show that ReCal outperforms other calibration methods on multiple datasets, especially, on large-scale datasets such as ImageNet.