Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks (2402.19460v2)

Abstract: Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks, including abstained prediction, out-of-distribution detection, and aleatoric uncertainty quantification. The latest goal is disentanglement: the construction of multiple estimators that are each tailored to one and only one source of uncertainty. This paper presents the first benchmark of uncertainty disentanglement. We reimplement and evaluate a comprehensive range of uncertainty estimators, from Bayesian over evidential to deterministic ones, across a diverse range of uncertainty tasks on ImageNet. We find that, despite recent theoretical endeavors, no existing approach provides pairs of disentangled uncertainty estimators in practice. We further find that specialized uncertainty tasks are harder than predictive uncertainty tasks, where we observe saturating performance. Our results provide both practical advice for which uncertainty estimators to use for which specific task, and reveal opportunities for future research toward task-centric and disentangled uncertainties. All our reimplementations and Weights & Biases logs are available at https://github.com/bmucsanyi/untangle.

• The paper demonstrates that current methods often fail to effectively disentangle aleatoric and epistemic uncertainties.
• It reveals that uncertainty estimators perform differently across tasks, highlighting the need to match methods with specific applications.
• The study underscores the importance of developing robust techniques to enhance uncertainty quantification for safer deep learning deployments.

Benchmarking Uncertainty Disentanglement in Deep Learning Models

Deep Learning and Uncertainty Quantification

Deep learning models have achieved remarkable success across a vast array of tasks, ranging from image recognition to natural language processing. However, these models often exude confidence in their predictions, even when those predictions are wrong. This issue highlights the importance of uncertainty quantification (UQ) in deep learning—a field striving to understand and mitigate the overconfidence of these models, ensuring safer and more reliable applications.

The Challenge of Uncertainty Disentanglement

A particular challenge within UQ is the disentanglement of uncertainty into aleatoric and epistemic components. Aleatoric uncertainty is inherent to the data, often due to noise or ambiguity, while epistemic uncertainty stems from the model's lack of knowledge and can, in theory, be reduced given more data. The ability to separate these uncertainty components is crucial for tasks such as active learning, where identifying what the model does not know can guide data collection efforts. However, recent literature suggests that achieving disentanglement in practice is challenging, with many proposed techniques falling short when applied to large-scale benchmarks like ImageNet.

Investigating Uncertainty Estimators

The paper dissects numerous uncertainty estimators and the efficacy of popular disentanglement formulas over several practically defined tasks on the ImageNet dataset. These tasks range from abstained prediction to out-of-distribution detection, each testing different facets of an uncertainty estimator's capabilities. Through comprehensive experimentation, the paper illuminates two critical insights:

1. Disentanglement Remains Elusive: Despite the theoretical appeal of separating aleatoric and epistemic uncertainty, the paper provides empirical evidence that current methods intertwine these uncertainties more often than not. Such findings suggest that the community's understanding of disentanglement needs reevaluation.
2. Task-Specific Performance: The analysis reveals that no universal uncertainty estimator excels across all tasks. Instead, specific uncertainty quantification methods outperform others on particular tasks, underscoring the importance of aligning the choice of an estimator with the intended application.

Implications and Future Directions

These insights have profound implications for both practitioners and researchers. For practitioners, the findings guide the selection of uncertainty estimation methods based on the specific needs of their applications. Researchers are encouraged to develop more robust and disentangled uncertainty estimators, considering the practical limitations unveiled in this paper. Furthermore, the discrepancies in method performance across different datasets—particularly between CIFAR-10 and ImageNet—highlight the necessity of evaluating uncertainty quantification approaches on diverse and large-scale benchmarks to ensure generalizability.

Conclusion

The paper underscores the nuanced nature of uncertainty quantification in deep learning, challenging the community to develop more sophisticated approaches that can genuinely disentangle aleatoric and epistemic uncertainties. As the field progresses, achieving this goal will not only bolster the reliability of deep learning models but also unlock new avenues for machine learning applications that can responsibly acknowledge their limitations.