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Joint Energy-based ...
Joint Energy-based Model for Deep Probabilistic Regression
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- Liu, Xixi, 1995 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Lin, Che-Tsung, 1979 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Zach, Christopher, 1974 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- ISBN 9781665490627
- 2022
- 2022
- Engelska.
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Ingår i: Proceedings - International Conference on Pattern Recognition. - 1051-4651. - 9781665490627 ; 2022, s. 2693-2699
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Abstract
Ämnesord
Stäng
- It is desirable that a deep neural network trained on a regression task does not only achieve high prediction accuracy, but its prediction posteriors are also well-calibrated, especially in safety-critical settings. Recently, energy-based models specifically to enrich regression posteriors have been proposed and achieve state-of-art results in object detection tasks. However, applying these models at prediction time is not straightforward as the resulting inference methods require to minimize an underlying energy function. Furthermore, these methods empirically do not provide accurate prediction uncertainties. Inspired by recent joint energy-based models for classification, in this work we propose to utilize a joint energy model for regression tasks and describe architectural differences needed in this setting. Within this frame-work, we apply our methods to three computer vision regression tasks. We demonstrate that joint energy-based models for deep probabilistic regression improve the calibration property, do not require expensive inference, and yield competitive accuracy in terms of the mean absolute error (MAE).
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
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