A General Framework for Uncertainty Estimation in Deep Learning (1907.06890v4)

Abstract: Neural networks predictions are unreliable when the input sample is out of the training distribution or corrupted by noise. Being able to detect such failures automatically is fundamental to integrate deep learning algorithms into robotics. Current approaches for uncertainty estimation of neural networks require changes to the network and optimization process, typically ignore prior knowledge about the data, and tend to make over-simplifying assumptions which underestimate uncertainty. To address these limitations, we propose a novel framework for uncertainty estimation. Based on Bayesian belief networks and Monte-Carlo sampling, our framework not only fully models the different sources of prediction uncertainty, but also incorporates prior data information, e.g. sensor noise. We show theoretically that this gives us the ability to capture uncertainty better than existing methods. In addition, our framework has several desirable properties: (i) it is agnostic to the network architecture and task; (ii) it does not require changes in the optimization process; (iii) it can be applied to already trained architectures. We thoroughly validate the proposed framework through extensive experiments on both computer vision and control tasks, where we outperform previous methods by up to 23% in accuracy.

A General Framework for Uncertainty Estimation in Deep Learning

The paper, "A General Framework for Uncertainty Estimation in Deep Learning," presents a comprehensive approach to tackling the challenge of uncertainty quantification in neural networks, which is pivotal for deploying these models in real-time robotic applications. The authors critique existing methods for their inherent limitations—such as the need for architectural changes, training complexities, and the ignorance of prior data knowledge—and propose an innovative solution leveraging Bayesian beliefs and Monte-Carlo methods.

Methodological Innovations

The framework delineated in the paper is centered around effectively capturing the various sources of uncertainty that afflict prediction tasks in neural networks. Key contributions of this approach include:

1. Architecture and Task Agnosticism: A salient feature of the proposed framework is its universality. It can be seamlessly integrated into pre-existing network architectures without necessitating modifications, thereby preserving the integrity of any well-trained models already in use.
2. Comprehensive Uncertainty Modeling: By blending Bayesian belief networks with Monte-Carlo dropout sampling, the framework robustly approximates the prediction distribution, enabling better capturing of both model and data uncertainties. Unlike traditional methods, it incorporates sensor noise and other sources of uncertainty directly into its computations, using established methods such as Assumed Density Filtering (ADF) for this propagation.
3. Theoretical and Empirical Validation: The paper articulates the superiority of this framework through a balance of mathematical rigor and empirical substantiation. Notably, the proposed framework theoretically demonstrates improved capability to model the interaction between model and data uncertainty—a key advancement over extant approaches where these uncertainties are often modeled independently.

Experimental Evaluation

The validity of the framework is scrutinized through its application to a variety of challenging tasks, prominently in the domains of computer vision and robotics:

• Steering Angle Prediction: Incorporating uncertainty estimates in this widely-studied task adds a layer of safety and reliability in autonomous driving systems. The proposed methodology improves on prediction accuracy by up to 23% compared to existing alternatives, with a commendable balance in accuracy and uncertainty quality denoted by metrics such as RMSE and NLL.
• Object Motion Prediction and Recognition: The framework is robustly demonstrated on object motion tasks within dynamic environments, significantly outperforming traditional approaches in terms of precision and certainty in predictions, indicating its potential utility in applications such as dynamic obstacle avoidance.
• Closed-Loop Control of Quadrotors: This application underscores the framework's potential for integration into feedback control systems—an area of critical importance for aerospace and robotics applications. By modulating control actions based on predicted uncertainties, enhanced performance in trajectory tracking of quadrotors is observed.

Implications and Future Directions

The framework proposed in this paper has wide-ranging implications for the practical use of deep learning systems in safety-critical and time-sensitive environments. By minimizing the need for network reconfiguration and ensuring accurate uncertainty predictions, this approach facilitates the seamless incorporation of deep learning models into existing workflows within robotics and AI applications.

Looking forward, future advancements could focus on addressing the computational overhead associated with Monte-Carlo sampling. Streamlining the inference process to maintain the rich uncertainty characterization while enhancing computational efficiency will be crucial, particularly for real-time applications involving large-scale or resource-constrained deployments. As the field progresses, integrating novel theoretical insights from information theory and probabilistic modeling into this framework may further refine the uncertainty estimation capabilities.

In summary, this framework constitutes a substantial contribution to those fields where robust uncertainty estimation can greatly enhance the utility and reliability of deep learning-based systems. Its integration into various applications could markedly advance the current state-of-the-art in both AI and robotic systems.