Deterministic Uncertainty Estimation for Multi-Modal Regression With Deep Neural Networks
Prediction interval (PI) is a common method to represent predictive uncertainty in regression by deep neural networks. This paper proposes an extension of the prediction interval by using a union of disjoint intervals. Since previous PI methods assumed a single-interval PI (one lower and upper bound...
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| Format: | Article |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10909531/ |
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| author | Jaehak Cho Jae Myung Kim Seungyub Han Jungwoo Lee |
| author_facet | Jaehak Cho Jae Myung Kim Seungyub Han Jungwoo Lee |
| author_sort | Jaehak Cho |
| collection | DOAJ |
| description | Prediction interval (PI) is a common method to represent predictive uncertainty in regression by deep neural networks. This paper proposes an extension of the prediction interval by using a union of disjoint intervals. Since previous PI methods assumed a single-interval PI (one lower and upper bound), it suffers from performance degradation in uncertainty estimation when the conditional density function is multi-modal. This paper demonstrates the need to include multi-modality in uncertainty estimation for regression. To address the issue, we propose a novel method that generates a union of disjoint PI’s. With UCI benchmark experiments, the proposed method is shown to improve over current state-of-the-art uncertainty quantification methods, reducing an average PI width by over <inline-formula> <tex-math notation="LaTeX">$27~\%$ </tex-math></inline-formula>. With qualitative experiments, it is shown that multi-modality often exists in real-world datasets, and our method produces high-quality PI’s compared to existing PI methods. |
| format | Article |
| id | doaj-art-b65e42e9e75e4553a4f832a2a011ccc9 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b65e42e9e75e4553a4f832a2a011ccc92025-08-20T03:01:28ZengIEEEIEEE Access2169-35362025-01-0113452814528910.1109/ACCESS.2025.354791110909531Deterministic Uncertainty Estimation for Multi-Modal Regression With Deep Neural NetworksJaehak Cho0Jae Myung Kim1Seungyub Han2https://orcid.org/0009-0001-8704-8968Jungwoo Lee3https://orcid.org/0000-0002-6804-980XCognitive and Machine Learning Laboratory, Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, South KoreaExplainable Machine Learning Laboratory, Cluster of Excellence Machine Learning, University of Tübingen, Tübingen, GermanyCognitive and Machine Learning Laboratory, Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, South KoreaCognitive and Machine Learning Laboratory, Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, South KoreaPrediction interval (PI) is a common method to represent predictive uncertainty in regression by deep neural networks. This paper proposes an extension of the prediction interval by using a union of disjoint intervals. Since previous PI methods assumed a single-interval PI (one lower and upper bound), it suffers from performance degradation in uncertainty estimation when the conditional density function is multi-modal. This paper demonstrates the need to include multi-modality in uncertainty estimation for regression. To address the issue, we propose a novel method that generates a union of disjoint PI’s. With UCI benchmark experiments, the proposed method is shown to improve over current state-of-the-art uncertainty quantification methods, reducing an average PI width by over <inline-formula> <tex-math notation="LaTeX">$27~\%$ </tex-math></inline-formula>. With qualitative experiments, it is shown that multi-modality often exists in real-world datasets, and our method produces high-quality PI’s compared to existing PI methods.https://ieeexplore.ieee.org/document/10909531/Deep learninguncertainty estimationmulti-modal distributionregression |
| spellingShingle | Jaehak Cho Jae Myung Kim Seungyub Han Jungwoo Lee Deterministic Uncertainty Estimation for Multi-Modal Regression With Deep Neural Networks IEEE Access Deep learning uncertainty estimation multi-modal distribution regression |
| title | Deterministic Uncertainty Estimation for Multi-Modal Regression With Deep Neural Networks |
| title_full | Deterministic Uncertainty Estimation for Multi-Modal Regression With Deep Neural Networks |
| title_fullStr | Deterministic Uncertainty Estimation for Multi-Modal Regression With Deep Neural Networks |
| title_full_unstemmed | Deterministic Uncertainty Estimation for Multi-Modal Regression With Deep Neural Networks |
| title_short | Deterministic Uncertainty Estimation for Multi-Modal Regression With Deep Neural Networks |
| title_sort | deterministic uncertainty estimation for multi modal regression with deep neural networks |
| topic | Deep learning uncertainty estimation multi-modal distribution regression |
| url | https://ieeexplore.ieee.org/document/10909531/ |
| work_keys_str_mv | AT jaehakcho deterministicuncertaintyestimationformultimodalregressionwithdeepneuralnetworks AT jaemyungkim deterministicuncertaintyestimationformultimodalregressionwithdeepneuralnetworks AT seungyubhan deterministicuncertaintyestimationformultimodalregressionwithdeepneuralnetworks AT jungwoolee deterministicuncertaintyestimationformultimodalregressionwithdeepneuralnetworks |