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I would like to implement a convolutional autoencoder in Tensorflow, but it is not clear how the decoder part should work.

Each layer of the encoding, is a convolutional layer with activation function and then a pooling layer.

But how will the decoding work? I know that I have to add padding in each layer, but what will be the reverse of the convolution? How will it reproduce the original data from much less variables and the padding?

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Transposed Convolutions is what you are looking for, for more details take a look here: https://medium.com/towards-data-science/types-of-convolutions-in-deep-learning-717013397f4d

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