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Uncertainty Calibration for Deep Audio Classifiers
[article]
2022
arXiv
pre-print
Although deep Neural Networks (DNNs) have achieved tremendous success in audio classification tasks, their uncertainty calibration are still under-explored. A well-calibrated model should be accurate when it is certain about its prediction and indicate high uncertainty when it is likely to be inaccurate. In this work, we investigate the uncertainty calibration for deep audio classifiers. In particular, we empirically study the performance of popular calibration methods: (i) Monte Carlo Dropout,
arXiv:2206.13071v1
fatcat:uuvfzpkgvzfxfbol2lsyahcpmm