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

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

19 Jun 2023arXiv:2306.10989archive 2025-07-28

Seungjin Jung, Seungmo Seo, Yonghyun Jeong, Jongwon Choi

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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class_based_temperature_scaler seungjinjung/sctl/models/scaler.py official repository ran · metamorphic tier: deterministic MIT (permissive) · 40773557ed94ae9a · report
adam SeungjinJung/SCTL/models/optimizer.py official repository unverified MIT (permissive) · 27010e72471f6c1d · report
calibrator_mapping SeungjinJung/SCTL/models/utils.py official repository unverified MIT (permissive) · 47cc5a7673231462 · report
dataloader SeungjinJung/SCTL/models/dataloader.py official repository unverified MIT (permissive) · 72b0d132e8a4f5c4 · report
dataloader SeungjinJung/SCTL/models/utils.py official repository unverified MIT (permissive) · 768d7c636555f7fd · report
dataset_mapping SeungjinJung/SCTL/models/utils.py official repository unverified MIT (permissive) · a189dc26ca7b7fdf · report
lbfgs SeungjinJung/SCTL/models/optimizer.py official repository unverified MIT (permissive) · 26016a0b4c7d54b2 · report
lbfgs_schedule SeungjinJung/SCTL/models/optimizer.py official repository unverified MIT (permissive) · 6464f7143a097230 · report

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