Post-hoc Uncertainty Calibration for Domain Drift Scenarios (2012.10988v2)

Abstract: We address the problem of uncertainty calibration. While standard deep neural networks typically yield uncalibrated predictions, calibrated confidence scores that are representative of the true likelihood of a prediction can be achieved using post-hoc calibration methods. However, to date the focus of these approaches has been on in-domain calibration. Our contribution is two-fold. First, we show that existing post-hoc calibration methods yield highly over-confident predictions under domain shift. Second, we introduce a simple strategy where perturbations are applied to samples in the validation set before performing the post-hoc calibration step. In extensive experiments, we demonstrate that this perturbation step results in substantially better calibration under domain shift on a wide range of architectures and modelling tasks.

Post-hoc Uncertainty Calibration for Domain Drift Scenarios

Uncertainty calibration is a critical aspect of machine learning model deployment, ensuring that predicted confidence scores align with real-world outcomes. In this paper, the authors focus on the challenges associated with uncertainty calibration within domain drift scenarios, emphasizing the limitations of existing post-hoc calibration methods when predicting under conditions of domain shift.

Overview of Contributions

The paper introduces a novel approach to post-hoc uncertainty calibration aimed at addressing over-confidence in predictions encountered during domain shift. Two primary contributions are outlined:

1. Analysis of Existing Methods: The authors demonstrate that current post-hoc calibration techniques often result in over-confident predictions when there is a shift in the input data distribution away from the domain on which the model was trained. This is especially problematic in dynamic environments where accurate and calibrated uncertainty estimates are crucial for decision-making processes.
2. Proposed Calibration Strategy: A perturbation-based approach is introduced where samples within the validation set are transformed using random additive noise prior to performing the calibration step. This enables the calibration process to incorporate a wider range of domain drift scenarios, leading to substantial improvements in handling uncertainty during out-of-distribution (OOD) predictions across varied architectures and datasets.

Experimental Analysis

The validation of this approach involves extensive empirical analysis across multiple datasets and model architectures. Tests incorporate 28 distinct perturbation types, including affine transformations and other image distortions. The results are standardized against well-known neural networks such as VGG19, ResNet50, DenseNet121, MobileNetv2, and others trained on CIFAR-10 and ImageNet datasets.

• Calibration Error (ECE): The paper reports a significant reduction in ECE for models calibrated using the proposed perturbation strategy. For instance, mean ECE decreased notably across all test domain drift scenarios, illustrating improved model calibration under substantial perturbations.
• Entropy and Accuracy: Evaluation metrics indicate a consistent alignment between model uncertainty (entropy) and accuracy at various perturbation levels. Models employing perturbation-based validation demonstrated superior performance in maintaining calibration throughout the domain drift continuum, from in-domain to truly OOD situations.
• Real-world Application: Testing extends to the ObjectNet dataset to simulate real-world domain shift scenarios, confirming that the perturbation-based tuning approach enhances predictive calibration in diverse contexts such as different object viewpoints and lighting conditions.

Practical and Theoretical Implications

This novel calibration approach holds practical significance in domains that involve dynamic data environments, such as autonomous systems, medical diagnostics, and industrial monitoring. The ability to maintain accuracy and reliability of confidence scores despite gradual or abrupt changes in data distribution can lead to more robust and trustworthy AI systems.

On the theoretical frontier, the proposed method challenges the current post-hoc calibration paradigm by demonstrating versatile adaptability through predictive perturbations. The balance between expressive power and calibration consistency at various uncertainty levels is a promising direction for future research.

Future Prospects

Future work may explore the integration of this perturbation-based strategy within ensemble learning frameworks or probabilistic models for enhanced scalability and adaptability. Furthermore, investigating the potential of combining intrinsically uncertainty-aware models with improved post-hoc techniques might bridge the gap towards achieving both precision and calibration in complex machine learning ecosystems.

In summary, the paper provides a comprehensive evaluation and a compelling augmentation to post-hoc uncertainty calibration techniques, particularly beneficial in scenarios characterized by shifting data distributions.