Beyond Overconfidence: Foundation Models Redefine Calibration in Deep Neural Networks
This addresses the problem of reliable uncertainty calibration for deploying deep neural networks in high-stakes applications, revealing complex effects that challenge existing paradigms, though it is incremental in exploring underexplored properties.
The paper investigates the calibration behavior of foundation models, finding they tend to be underconfident in in-distribution predictions with higher calibration errors, but show improved calibration under distribution shifts, and post-hoc techniques are effective in-distribution but less reliable under severe shifts.
Reliable uncertainty calibration is essential for safely deploying deep neural networks in high-stakes applications. Deep neural networks are known to exhibit systematic overconfidence, especially under distribution shifts. Although foundation models such as ConvNeXt, EVA and BEiT have demonstrated significant improvements in predictive performance, their calibration properties remain underexplored. This paper presents a comprehensive investigation into the calibration behavior of foundation models, revealing insights that challenge established paradigms. Our empirical analysis shows that these models tend to be underconfident in in-distribution predictions, resulting in higher calibration errors, while demonstrating improved calibration under distribution shifts. Furthermore, we demonstrate that foundation models are highly responsive to post-hoc calibration techniques in the in-distribution setting, enabling practitioners to effectively mitigate underconfidence bias. However, these methods become progressively less reliable under severe distribution shifts and can occasionally produce counterproductive results. Our findings highlight the complex, non-monotonic effects of architectural and training innovations on calibration, challenging established narratives of continuous improvement.