Joint Energy-based Model for Deep Probabilistic Regression
Paper in proceeding, 2022

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).

Author

Xixi Liu

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Che-Tsung Lin

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Christopher Zach

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Proceedings - International Conference on Pattern Recognition

10514651 (ISSN)

Vol. 2022-August 2693-2699
9781665490627 (ISBN)

26th International Conference on Pattern Recognition, ICPR 2022
Montréal Québec, Canada,

Subject Categories

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/ICPR56361.2022.9955636

ISBN

9781665490627

More information

Latest update

7/22/2024