In the previous section, we learned about autoencoders and implemented them in PyTorch. While we have implemented them, one convenience that we had through the dataset was that each image has only 1 channel (each image was represented as a black and white image) and the images are relatively small (28 x 28). Hence the network flattened the input and was able to train on 784 (28*28) input values to predict 784 output values. However, in reality, we will encounter images that have 3 channels and are much bigger than a 28 x 28 image.
In this section, we will learn about implementing a convolutional autoencoder that is able to work on multi-dimensional input images. However, for the purpose of comparison with vanilla autoencoders, we will work on the same MNIST dataset that we worked on in the previous section, but modify the network in such a way that we now build a convolutional autoencoder and not a vanilla autoencoder.
A convolutional autoencoder...
