The many variations of Implicit Neural Representations (INRs), where a neural
network is trained as a continuous representation of a signal, have tremendous
practical utility for downstream tasks including novel view synthesis, video
compression, and image superresolution. Unfortunately, the inner workings of
these networks are seriously under-studied. Our work, eXplaining the Implicit
Neural Canvas (XINC), is a unified framework for explaining properties of INRs
by examining the strength of each neuron's contribution to each output pixel.
We call the aggregate of these contribution maps the Implicit Neural Canvas and
we use this concept to demonstrate that the INRs which we study learn to
''see'' the frames they represent in surprising ways. For example, INRs tend to
have highly distributed representations. While lacking high-level object
semantics, they have a significant bias for color and edges, and are almost
entirely space-agnostic. We arrive at our conclusions by examining how objects
are represented across time in video INRs, using clustering to visualize
similar neurons across layers and architectures, and show that this is
dominated by motion. These insights demonstrate the general usefulness of our
analysis framework. Our project page is available at
this https URL

Implicit Neural Representations are analyzed and explained through the framework of eXplaining the Implicit Neural Canvas, revealing surprising ways in which these representations 'see' the frames, such as highly distributed representations biased towards color and edges and minimal space-dependency, with motion dominating object representation across time in video INRs.