  The existing deep learning models suffer from out-of-distribution (o.o.d.)
performance drop in computer vision tasks. In comparison, humans have a
remarkable ability to interpret images, even if the scenes in the images are
rare, thanks to the systematicity of acquired knowledge. This work focuses on
1) the acquisition of systematic knowledge of 2D transformations, and 2)
architectural components that can leverage the learned knowledge in image
classification tasks in an o.o.d. setting. With a new training methodology
based on synthetic datasets that are constructed under the causal framework,
the deep neural networks acquire knowledge from semantically different domains
(e.g. even from noise), and exhibit certain level of systematicity in parameter
estimation experiments. Based on this, a novel architecture is devised
consisting of a classifier, an estimator and an identifier (abbreviated as
"CED"). By emulating the "hypothesis-verification" process in human visual
perception, CED improves the classification accuracy significantly on test sets
under covariate shift.
