TL;DR
- Feature density calibrates prediction confidence: DAC uses nearest-neighbor information in hidden representations.
- Calibration remains reliable under distribution shifts: Improves uncertainty estimates on shifted and out-of-distribution inputs.
- Accuracy is preserved across diverse models: Combines with post-hoc calibration methods and is evaluated across architectures and datasets.
Type
Publication
ICML 2023
