MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG Analysis. (arXiv:2311.04224v1 [eess.SP])

We introduce MELEP, which stands for Muti-label Expected Log of Empirical
Predictions, a novel measure to estimate how effective it is to transfer
knowledge from a pre-trained model to a downstream task in a multi-label
settings. The measure is generic to work with new target data having a
different label set from source data. It is also computationally efficient,
only requires forward passing the downstream dataset through the pre-trained
model once. To the best of our knowledge, we are the first to develop such a
transferability metric for multi-label ECG classification problems. Our
experiments show that MELEP can predict the performance of pre-trained
convolutional and recurrent deep neural networks, on small and imbalanced ECG
data. Specifically, strong correlation coefficients, with absolute values
exceeding 0.6 in most cases, were observed between MELEP and the actual average
F1 scores of the fine-tuned models.



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