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- Title
Estimating Classification Accuracy for Unlabeled Datasets Based on Block Scaling.
- Authors
You, Shingchern D.; Kai-Rong Lin; Chien-Hung Liu
- Abstract
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 α, where α is equal to 0 in the experiments. The ratio of perturbed spectrograms that have different prediction labels than the original spectrogram to the total number of perturbed spectrograms indicates how much of the spectrogram is crucial for the prediction. Thus, this ratio is inversely correlated with the accuracy of the dataset. The BSQ approach demonstrates satisfactory estimation accuracy in experiments when compared with various other approaches. When using only the Jamendo and FMA datasets, the estimation accuracy experiences an average error of 4.9% and 1.8%, respectively. Moreover, the BSQ approach holds advantages over some of the comparison counterparts. Overall, it presents a promising approach for estimating the accuracy of a deep network model.
- Subjects
CONVOLUTIONAL neural networks; SPECTROGRAMS
- Publication
International Journal of Engineering & Technology Innovation, 2023, Vol 13, Issue 4, p313
- ISSN
2223-5329
- Publication type
Article
- DOI
10.46604/ijeti.2023.11975