  An important component for generalization in machine learning is to uncover
underlying latent factors of variation as well as the mechanism through which
each factor acts in the world. In this paper, we test whether 17 unsupervised,
weakly supervised, and fully supervised representation learning approaches
correctly infer the generative factors of variation in simple datasets
(dSprites, Shapes3D, MPI3D) from controlled environments, and on our
contributed CelebGlow dataset. In contrast to prior robustness work that
introduces novel factors of variation during test time, such as blur or other
(un)structured noise, we here recompose, interpolate, or extrapolate only
existing factors of variation from the training data set (e.g., small and
medium-sized objects during training and large objects during testing). Models
that learn the correct mechanism should be able to generalize to this
benchmark. In total, we train and test 2000+ models and observe that all of
them struggle to learn the underlying mechanism regardless of supervision
signal and architectural bias. Moreover, the generalization capabilities of all
tested models drop significantly as we move from artificial datasets towards
more realistic real-world datasets. Despite their inability to identify the
correct mechanism, the models are quite modular as their ability to infer other
in-distribution factors remains fairly stable, providing only a single factor
is out-of-distribution. These results point to an important yet understudied
problem of learning mechanistic models of observations that can facilitate
generalization.
