  Neural networks trained with SGD were recently shown to rely preferentially
on linearly-predictive features and can ignore complex, equally-predictive
ones. This simplicity bias can explain their lack of robustness out of
distribution (OOD). The more complex the task to learn, the more likely it is
that statistical artifacts (i.e. selection biases, spurious correlations) are
simpler than the mechanisms to learn. We demonstrate that the simplicity bias
can be mitigated and OOD generalization improved. We train a set of similar
models to fit the data in different ways using a penalty on the alignment of
their input gradients. We show theoretically and empirically that this induces
the learning of more complex predictive patterns. OOD generalization
fundamentally requires information beyond i.i.d. examples, such as multiple
training environments, counterfactual examples, or other side information. Our
approach shows that we can defer this requirement to an independent model
selection stage. We obtain SOTA results in visual recognition on biased data
and generalization across visual domains. The method - the first to evade the
simplicity bias - highlights the need for a better understanding and control of
inductive biases in deep learning.
