A Survey on Uncertainty Quantification Methods for Deep Learning

TL;DR

This survey categorizes uncertainty quantification methods for deep neural networks based on uncertainty sources, compares their strengths and limitations, and discusses applications and future research directions in high-stakes AI tasks.

Contribution

It introduces a taxonomy of UQ methods based on uncertainty sources, addressing a gap in existing surveys that focus on architecture or Bayesian approaches.

Findings

Categorizes UQ methods by uncertainty sources like data and model uncertainty.

Highlights advantages and disadvantages of each UQ category.

Discusses applications such as active learning and out-of-distribution detection.

Abstract

Deep neural networks (DNNs) have achieved tremendous success in computer vision, natural language processing, and scientific and engineering domains. However, DNNs can make unexpected, incorrect, yet overconfident predictions, leading to serious consequences in high-stakes applications such as autonomous driving, medical diagnosis, and disaster response. Uncertainty quantification (UQ) estimates the confidence of DNN predictions in addition to their accuracy. In recent years, many UQ methods have been developed for DNNs. It is valuable to systematically categorize these methods and compare their strengths and limitations. Existing surveys mostly categorize UQ methodologies by neural network architecture or Bayesian formulation, while overlooking the uncertainty sources each method addresses, making it difficult to select an appropriate approach in practice. To fill this gap, this paper presents a taxonomy of UQ methods for DNNs based on uncertainty sources (e.g., data versus model uncertainty). We summarize the advantages and disadvantages of each category, and illustrate how UQ can be applied to machine learning problems (e.g., active learning, out-of-distribution robustness, and deep reinforcement learning). We also identify future research directions, including UQ for large language models (LLMs), AI-driven scientific simulations, and deep neural networks with structured outputs.