핵심 개념
MixupMP proposes a novel approach to uncertainty quantification in deep learning, outperforming existing methods.
초록
The content discusses the problem of uncertainty quantification in deep learning and introduces MixupMP as a method to address limitations in existing approaches. It compares DE and BB, presents the concept of Martingale posteriors, and details the MixupMP methodology. Experimental results on various datasets are provided, showing the superior performance of MixupMP.
- Introduction
- Importance of uncertainty quantification in deep learning for safety-critical applications.
- Overview of Bayesian neural networks and non-Bayesian approaches like DE.
- Martingale Posteriors
- Introduction to Martingale posteriors as an alternative to classical Bayesian inference.
- Comparison between DE and BB, highlighting their equivalency under certain conditions.
- Mixup Martingale Posteriors
- Proposal of MixupMP as a new predictive distribution approach for deep learning models.
- Explanation of how MixupMP incorporates data augmentation techniques for improved predictive performance.
- Experiments
- Evaluation of MixupMP's performance compared to other methods on various datasets.
- Ablation study on the impact of hyperparameters in MixupMP.
- Robustness analysis under distribution shift using CIFAR10-C dataset.
- Related Works
- Comparison with other uncertainty quantification methods such as Laplace approximation and MC Dropout.
- Discussion
- Summary of findings showcasing the superiority of MixupMP in predictive performance and uncertainty calibration.
통계
DE is equivalent to BB under certain conditions.
MixupMP outperforms Laplace, MC Dropout, DE, and Mixup Ensemble.
MixupMP with r = 1.0 achieves best accuracy across all shift intensity levels.
인용구
"MixupMP proposes a novel martingale posterior approach that uses state-of-the-art data augmentation techniques."
"DE tends to be over-confident while Mixup Ensemble tends to be under-confident."