Calibration of ordinal regression networks

• URL: http://arxiv.org/abs/2410.15658v2
• Date: Fri, 15 Nov 2024 09:34:23 GMT
• Title: Calibration of ordinal regression networks
• Authors: Daehwan Kim, Haejun Chung, Ikbeom Jang,
• Abstract summary: Deep neural networks are not well-calibrated and often produce over-confident predictions. We propose a novel loss function that introduces order-aware calibration. It incorporates soft ordinal encoding and order-aware regularization to enforce both calibration and unimodality.
• Score: 1.2242167538741824
• License:
• Abstract: Recent studies have shown that deep neural networks are not well-calibrated and often produce over-confident predictions. The miscalibration issue primarily stems from using cross-entropy in classifications, which aims to align predicted softmax probabilities with one-hot labels. In ordinal regression tasks, this problem is compounded by an additional challenge: the expectation that softmax probabilities should exhibit unimodal distribution is not met with cross-entropy. The ordinal regression literature has focused on learning orders and overlooked calibration. To address both issues, we propose a novel loss function that introduces order-aware calibration, ensuring that prediction confidence adheres to ordinal relationships between classes. It incorporates soft ordinal encoding and order-aware regularization to enforce both calibration and unimodality. Extensive experiments across three popular ordinal regression benchmarks demonstrate that our approach achieves state-of-the-art calibration without compromising accuracy.

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