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yauangon t1_j67hram wrote

I'm trying to improve a CNN encoder, as a feature extractor for an AMT (automatic music transcription) model. As the model must be small and fast (for mobile deployment), we are limited to about 3-6 layers of 1D-CNN. I want to improve the encoder with residual block (of ResNet), but my question is: I don't known if Residual block would benefit on such a shallow CNN architecture? Thank everyone :D

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Anvilondre t1_j6a8v34 wrote

Probably not. The idea of ResNets is to remove the vanishing gradients that normally occur in very deep networks. In my experience it can often do worse than better, but you can try DenseNets instead.

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yauangon t1_j6h8s2s wrote

I will give it a shot :D Thank you a lot :D

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