Efficacy Score for Data Augmentations
Version 1.0.2 (2,07 KB) von
David Heise
A function for calculating the efficacy score, or eff-score, of a data augmentation prior to training and empirical testing of a model.
This function will calculate the eff-score of a given augmentation as described in:
Heise, D. and Bear, H, "A Transparent Method for Visualizing and Scoring the Efficacy of Data Augmentations on Real-World Audio", submitted to IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, in review, 2023.
Inputs:
D - a square dissimilarity matrix, computed via the Visual Assessment of cluster Tendency (VAT) algorithm, of all the samples in the source data
k - an integer specifying the size of neighbourhoods (in pixels) to use for computing diversity (optional; by default, k = 3)
b - an integer specifying the number of class intervals to use for binning the set of diversity values, such as for producing a histogram (optional; by default, b = 20)
w - an integer specifying the width of each class interval (optional, computed automatically if not specified); w should be chosen such that (b*w) is just greater than the maximum diversity
Outputs:
score - the computed eff-score
b_out - the value of b used by the function
w_out - the value of w used by the function
h_fig - a handle to the produced histogram figure
Zitieren als
D. Heise and H. Bear, "Evaluating the Potential and Realized Impact of Data Augmentations", submitted to 2023 IEEE Symposium Series on Computational Intelligence, in review.
Kompatibilität der MATLAB-Version
Erstellt mit
R2023a
Kompatibel mit allen Versionen
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Version | Veröffentlicht | Versionshinweise | |
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1.0.2 | updated citation |
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1.0.1 | a number of small (but fatal) errors corrected |
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1.0.0 |