Convolutional autoencoders
Until now, we have seen that autoencoder inputs are images. So, it makes sense to ask whether a convolutional architecture can work better than the autoencoder architectures discussed previously.
We will analyze how the encoder and decoder work in convolutional autoencoders.
Encoder
The encoder consists of three convolutional layers. The number of features changes from 1, the input data, to 16 for the first convolutional layer, then from 16 to 32 for the second layer, and finally, from 32 to 64 for the final convolutional layer.
While transacting from one convolutional layer to another, the shape undergoes an image compression:
Data flow of encoding phase
Decoder
The decoder consists of three deconvolution layers arranged in sequence. For each deconvolution operation, we reduce the number of features to obtain an image that must be the same size as the original image. In addition to reducing the number of features, deconvolution involves a shape transformation of the...
