  Convolutional layers are a fundamental component of most image-related
models. These layers often implement by default a static padding policy (\eg
zero padding), to control the scale of the internal representations, and to
allow kernel activations centered on the border regions. In this work we
identify Padding Aware Neurons (PANs), a type of filter that is found in most
(if not all) convolutional models trained with static padding. PANs focus on
the characterization and recognition of input border location, introducing a
spatial inductive bias into the model (e.g., how close to the input's border a
pattern typically is). We propose a method to identify PANs through their
activations, and explore their presence in several popular pre-trained models,
finding PANs on all models explored, from dozens to hundreds. We discuss and
illustrate different types of PANs, their kernels and behaviour. To understand
their relevance, we test their impact on model performance, and find padding
and PANs to induce strong and characteristic biases in the data. Finally, we
discuss whether or not PANs are desirable, as well as the potential side
effects of their presence in the context of model performance, generalisation,
efficiency and safety.
