  "The power of a generalization system follows directly from its biases"
(Mitchell 1980). Today, CNNs are incredibly powerful generalisation systems --
but to what degree have we understood how their inductive bias influences model
decisions? We here attempt to disentangle the various aspects that determine
how a model decides. In particular, we ask: what makes one model decide
differently from another? In a meticulously controlled setting, we find that
(1.) irrespective of the network architecture or objective (e.g.
self-supervised, semi-supervised, vision transformers, recurrent models) all
models end up with a similar decision boundary. (2.) To understand these
findings, we analysed model decisions on the ImageNet validation set from epoch
to epoch and image by image. We find that the ImageNet validation set, among
others, suffers from dichotomous data difficulty (DDD): For the range of
investigated models and their accuracies, it is dominated by 46.0% "trivial"
and 11.5% "impossible" images (beyond label errors). Only 42.5% of the images
could possibly be responsible for the differences between two models' decision
boundaries. (3.) Only removing the "impossible" and "trivial" images allows us
to see pronounced differences between models. (4.) Humans are highly accurate
at predicting which images are "trivial" and "impossible" for CNNs (81.4%).
This implies that in future comparisons of brains, machines and behaviour, much
may be gained from investigating the decisive role of images and the
distribution of their difficulties.
