@article{M696EBFC6,
title = "Novelty Detection in Underwater Acoustic Environments Using Out-of-Distribution Detector for Neural Networks",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2025",
issn = "1226-4717",
doi = "10.7840/kics.2025.50.12.1839",
author = "Nayeon Kim, Chanjun Chun, Hong Kook Kim",
keywords = "Novelty detection, Out-of-Distribution, Detector for Neural Networks, , Temperature scaling, Input, perturbation",
abstract = "In this paper, we propose an ODIN-based novelty detection framework to effectively identify unknownacoustic signals in underwater environments. Specifically, temperature scaling and input perturbation are applied to the softmax output of a pre-trained classifier to induce differences between known and unknown samples, and the calibrated maximum softmax probability is used as a novelty score to perform novelty detection."
}