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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- uncertainty |
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- uncertainty-quantification |
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- code |
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--- |
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# SPC-UQ: A Post-hoc, Efficient, and Unified Uncertainty Quantification Framework |
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This repository contains the official code for **SPC-UQ** (Split-Point Consistency for Uncertainty Quantification), a post-hoc framework that jointly quantifies aleatoric and epistemic uncertainty with a single forward pass. |
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It accompanies the paper: |
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[**"Post-Hoc Split-Point Self-Consistency Verification for Efficient, Unified Quantification of Aleatoric and Epistemic Uncertainty in Deep Learning."**](https://arxiv.org/abs/2509.13262) |
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## Key Features |
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- **Post-hoc** – augments pre-trained network without architectural changes and retraining. |
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- **Unified** – supports both regression and classification tasks in deep learning. |
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- **Efficient** – produces aleatoric and epistemic uncertainty estimates in one forward pass. |
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- **Calibration** – provides mechanisms to calibrate aleatoric uncertainty and improve predictive reliability. |
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## Repository Structure |
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``` |
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Cubic_Regression/ # Toy cubic regression for fast demonstration. |
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MNIST_Classification/ # Digit classification for fast demonstration. |
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UCI_Benchmarks/ # Standard UCI regression datasets for scalar regression evaluation. |
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Monocular_Depth_Estimation/ # Monocular end-to-end image depth estimation for high-dimensional regression. |
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Image_Classification/ # CIFAR-10/100, ImageNet-1K for large-scale image classification. |
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Multimodal_Classification/ # LUMA multimodal benchmark (image/audio/text) for multimodal classification tasks. |
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``` |
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Each directory provides scripts to reproduce the corresponding experiments. |
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## Installation |
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We recommend using [conda](https://docs.conda.io/en/latest/) to manage dependencies. |
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All required packages and versions except Multimodal_Classification are specified in `environment.yml`. |
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### Step 1: Clone the repository |
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```bash |
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huggingface-cli download zzz0527/SPC-UQ --local-dir SPC-UQ |
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cd SPC-UQ |
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``` |
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### Step 2: Create and activate the environment |
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```bash |
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conda env create -f environment.yml |
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conda activate spc_uq |
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``` |
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## Usage |
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Each subdirectory corresponds to a specific benchmark. |
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To run an experiment, navigate into the corresponding folder and follow the instructions provided in its `README.md`. |
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### Quick Start |
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For a fast verification, we provide two lightweight benchmark tasks: |
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```bash |
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# Synthetic cubic regression |
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python Cubic_Regression/run_cubic_tests.py |
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# MNIST classification |
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python MNIST_Classification/run_cls_tests.py |
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``` |
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See the documentation in each subdirectory for details on dataset preparation, configuration options, and advanced usage. |
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## Citation |
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If you use SPC-UQ in your research, please cite our paper: |
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``` |
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@article{zhao2025post, |
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title={Post-Hoc Split-Point Self-Consistency Verification for Efficient, Unified Quantification of Aleatoric and Epistemic Uncertainty in Deep Learning}, |
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author={Zhao, Zhizhong and Chen, Ke}, |
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journal={arXiv preprint arXiv:2509.13262}, |
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year={2025} |
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} |
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``` |
