Estimating Classification Accuracy for Unlabeled Datasets Based on Block Scaling

Estimating Classification Accuracy for Unlabeled Datasets Based on Block Scaling

This paper proposes an approach called block scaling quality (BSQ) for estimating the prediction accuracy of a deep network model. The basic operation perturbs the input spectrogram by multiplying all values within a block by , where  is equal to 0 in the experiments. The ratio of perturbed spectro...

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Bibliographic Details
Main Authors: Shingchern D. You, Kai-Rong Lin, Chien-Hung Liu
Format: Article
Language:English
Published: Taiwan Association of Engineering and Technology Innovation 2023-09-01
Series:International Journal of Engineering and Technology Innovation
Subjects:
prediction accuracy estimation
unlabeled dataset
machine learning
convolutional neural network
vocal detection
Online Access:https://ojs.imeti.org/index.php/IJETI/article/view/11975

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