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An important component for generalization in machine learning is to uncoverunderlying latent factors of variation as well as the mechanism through whicheach factor acts in the world. In this paper, we test whether 17 unsupervised,weakly supervised, and fully supervised representation learning approachescorrectly infer the generative factors of variation in simple datasets(dSprites, Shapes3D, MPI3D) from controlled environments, and on ourcontributed CelebGlow dataset. In contrast to prior robustness work thatintroduces novel factors of variation during test time, such as blur or other(un)structured noise, we here recompose, interpolate, or extrapolate onlyexisting factors of variation from the training data set (e.g., small andmedium-sized objects during training and large objects during testing). Modelsthat learn the correct mechanism should be able to generalize to thisbenchmark. In total, we train and test 2000+ models and observe that all ofthem struggle to learn the underlying mechanism regardless of supervisionsignal and architectural bias. Moreover, the generalization capabilities of alltested models drop significantly as we move from artificial datasets towardsmore realistic real-world datasets. Despite their inability to identify thecorrect mechanism, the models are quite modular as their ability to infer otherin-distribution factors remains fairly stable, providing only a single factoris out-of-distribution. These results point to an important yet understudiedproblem of learning mechanistic models of observations that can facilitategeneralization.

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain