Calibration of ordinal regression networks

Daehwan Kim, Haejun Chung, Ikbeom Jang

Keywords: Ordinal regression, Calibration, Deep neural networks, Unimodality, Loss function, Soft ordinal encoding, Label smoothing, Order-aware calibration

TL;DR: We propose a loss function that introduces order-aware calibration in ordinal regression tasks, combining soft ordinal encoding and label-smoothing-based regularization to enforce both calibration and unimodality.

Abstract:

Recent studies have shown that deep neural networks are not well-calibrated and produce over-confident predictions.
The miscalibration issue primarily stems from the minimization of cross-entropy, 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. Rather, the ordinal regression literature has focused on unimodality and overlooked calibration. To address these 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 label-smoothing-based 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.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 14054