Scaling of Class-wise Training Losses for Post-hoc Calibration

• URL: http://arxiv.org/abs/2306.10989v1
• Date: Mon, 19 Jun 2023 14:59:37 GMT
• Title: Scaling of Class-wise Training Losses for Post-hoc Calibration
• Authors: Seungjin Jung, Seungmo Seo, Yonghyun Jeong, Jongwon Choi
• Abstract summary: We propose a new calibration method to synchronize the class-wise training losses. We design a new training loss to alleviate the variance of class-wise training losses by using multiple class-wise scaling factors. We validate the proposed framework by employing it in the various post-hoc calibration methods.
• Score: 6.0632746602205865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The class-wise training losses often diverge as a result of the various levels of intra-class and inter-class appearance variation, and we find that the diverging class-wise training losses cause the uncalibrated prediction with its reliability. To resolve the issue, we propose a new calibration method to synchronize the class-wise training losses. We design a new training loss to alleviate the variance of class-wise training losses by using multiple class-wise scaling factors. Since our framework can compensate the training losses of overfitted classes with those of under-fitted classes, the integrated training loss is preserved, preventing the performance drop even after the model calibration. Furthermore, our method can be easily employed in the post-hoc calibration methods, allowing us to use the pre-trained model as an initial model and reduce the additional computation for model calibration. We validate the proposed framework by employing it in the various post-hoc calibration methods, which generally improves calibration performance while preserving accuracy, and discover through the investigation that our approach performs well with unbalanced datasets and untuned hyperparameters.

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