Logit-based Uncertainty Measure in Classification

• URL: http://arxiv.org/abs/2107.02845v1
• Date: Tue, 6 Jul 2021 19:07:16 GMT
• Title: Logit-based Uncertainty Measure in Classification
• Authors: Huiyu Wu and Diego Klabjan
• Abstract summary: We introduce a new, reliable, and agnostic uncertainty measure for classification tasks called logit uncertainty. We show that this new uncertainty measure yields a superior performance compared to existing uncertainty measures on different tasks.
• Score: 18.224344440110862
• License: http://creativecommons.org/licenses/by/4.0/
• 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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