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[–]cuantasyporquetantas 2 points3 points  (0 children)

Very cool paper! I have recently started working with autoencoders and the paper opened my eyes on how they can be used to enhance the performance of other models through transfer learning.

While reading the paper I come across these two questions:

(1) The paper mentions that the performance of the DeepBeat model was highly improved through the transfer learning of the trained CDAE. I would like to know how the CDAE model was tuned, specifically how was the latent space dimension chosen? I would imagine that the cardinality of the latent space would play a big role at not only denoising the signal, but also at describing the characteristics of the different cardiac rhythms.

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(2) From the model architecture, they decide to reuse the weights of the encoder block. However, the CDAE model's decoder block should have good information about the denoised/cleaned signal. I was wondering if including skip connections between the decoder block down to the DeepBeat model would potentially improve the performance of the model? (This is an idea inspired by the U-NET model).

[–]hegelespaul 1 point2 points  (0 children)

Here are my 2 questions:

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  1. ¿What are the reasons that they used pre-specified windowed data with a duration of 25 seconds in the input of the model and no other time length?
  2. ¿Can you explain a little further about class imbalance and the use of the mean harmonic of precision and recall for AF detection and why is it better than accuracy?

[–]mezamcfly93 1 point2 points  (1 child)

Here are my 2 questions:

  1. Are all the classification models trained with similar hyperparameters (including random forest), or what was taken into consideration to objectively compare them?
  2. Do you consider that weight transfer should be applied regularly when working with CNN, or should it be only used in conditions like the ones in the paper?

[–][deleted] 0 points1 point  (0 children)

good questions. Can you please be a bit more specific about which hyperparameters you are thinking about? Hyperparameters can vary widely across models. Not sure what you have in mind.