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

• URL: http://arxiv.org/abs/2507.10638v2
• Date: Thu, 17 Jul 2025 06:11:45 GMT
• Title: ZClassifier: Temperature Tuning and Manifold Approximation via KL Divergence on Logit Space
• Authors: Shim Soon Yong,
• Abstract summary: We introduce a novel classification framework, ZClassifier, that replaces conventional deterministic logits with diagonal Gaussian-distributed logits.<n>Our method simultaneously addresses temperature scaling and manifold approximation by minimizing the Kullback-Leibler divergence between the predicted Gaussian distributions and a unit isotropic Gaussian.<n>This uncertainty unifies calibration and latent control in a principled probabilistic manner, enabling a natural interpretation of class confidence and geometric consistency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Kullback-Leibler (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 show that ZClassifier improves over softmax classifiers in robustness, calibration, and latent separation.

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