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Consider a stacked autoencoder setup where I have

Autoencoder1: noisy input > encoder > bottleneck1 > decoder
Autoencoder2: bottleneck1 output > encoder > bottleneck2 > decoder

When training the first autoencoder I'm using as input a noisy version of the original input, but what should I do with the second autoencoder? What I'm doing now is getting the output of bottleneck1 (with noisy input - the same input used for the initial training), applying noise again and using this to train the second autoencoder.

Is this correct or in the second autoencoder I should apply the noise to the output of bottleneck1 using the non-noisy input?

Hope this makes sense, thanks.

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The way in which you are creating this structure is not what is understood as a stacked autoencoder.

Autoencoders with multiple hidden layers are called stacked autoencoders or deep autoencoders.*

*See Hands-On Machine Learning with Scikit-Learn and Tensorflow by Aurélien Géron for a good overview of the different types of autoencoders. He actually has an example of a stacked denoising autoencoder in there.

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