  We employ a toolset -- dubbed Dr. Frankenstein -- to analyse the similarity
of representations in deep neural networks. With this toolset, we aim to match
the activations on given layers of two trained neural networks by joining them
with a stitching layer. We demonstrate that the inner representations emerging
in deep convolutional neural networks with the same architecture but different
initializations can be matched with a surprisingly high degree of accuracy even
with a single, affine stitching layer. We choose the stitching layer from
several possible classes of linear transformations and investigate their
performance and properties. The task of matching representations is closely
related to notions of similarity. Using this toolset, we also provide a novel
viewpoint on the current line of research regarding similarity indices of
neural network representations: the perspective of the performance on a task.
