Logit-based Uncertainty Measure in Classification

Huiyu Wu; Diego Klabjan

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arXiv · 2107.02845

Logit-based Uncertainty Measure in Classification

Huiyu Wu·Diego Klabjan

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Abstract

We introduce a new, reliable, and agnostic uncertainty measure for classification tasks called logit uncertainty. It is based on logit outputs of neural networks. We in particular show that this new uncertainty measure yields a superior performance compared to existing uncertainty measures on different tasks, including out of sample detection and finding erroneous predictions. We analyze theoretical foundations of the measure and explore a relationship with high density regions. We also demonstrate how to test uncertainty using intermediate outputs in training of generative adversarial networks. We propose two potential ways to utilize logit-based uncertainty in real world applications, and show that the uncertainty measure outperforms.

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Huiyu Wu, Diego Klabjan. 2021-07-06. Logit-based Uncertainty Measure in Classification. https://arxiv.org/abs/2107.02845

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