  Convolutional networks are large linear systems divided into layers and
connected by non-linear units. These units are the "articulations" that allow
the network to adapt to the input. To understand how a network manages to solve
a problem we must look at the articulated decisions in entirety. If we could
capture the actions of non-linear units for a particular input, we would be
able to replay the whole system back and forth as if it was always linear. It
would also reveal the actions of non-linearities because the resulting linear
system, a Linear Interpreter, depends on the input image. We introduce a
hooking layer, called a LinearScope, which allows us to run the network and the
linear interpreter in parallel. Its implementation is simple, flexible and
efficient. From here we can make many curious inquiries: how do these linear
systems look like? When the rows and columns of the transformation matrix are
images, how do they look like? What type of basis do these linear
transformations rely on? The answers depend on the problems presented, through
which we take a tour to some popular architectures used for classification,
super-resolution (SR) and image-to-image translation (I2I). For classification
we observe that popular networks use a pixel-wise vote per class strategy and
heavily rely on bias parameters. For SR and I2I we find that CNNs use
wavelet-type basis similar to the human visual system. For I2I we reveal
copy-move and template-creation strategies to generate outputs.
