ZClassifier: Temperature Tuning and Manifold Approximation via KL Divergence on Logit Space

Shim Soon Yong

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arXiv · 2507.10638

ZClassifier: Temperature Tuning and Manifold Approximation via KL Divergence on Logit Space

Shim Soon Yong

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Abstract

We introduce a novel classification framework, ZClassifier, that replaces conventional deterministic logits with diagonal Gaussian-distributed logits. Our method simultaneously addresses temperature scaling and manifold approximation by minimizing the KL divergence between the predicted Gaussian distributions and a unit isotropic Gaussian. This unifies uncertainty calibration and latent control in a principled probabilistic manner, enabling a natural interpretation of class confidence and geometric consistency. Experiments on CIFAR-10 and CIFAR-100 demonstrate that ZClassifier improves over softmax classifiers in robustness, calibration, and latent separation, with consistent benefits across small-scale and large-scale classification settings.

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Shim Soon Yong. 2025-07-14. ZClassifier: Temperature Tuning and Manifold Approximation via KL Divergence on Logit Space. https://arxiv.org/abs/2507.10638

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