  A major bottleneck in the real-world applications of machine learning models
is their failure in generalizing to unseen domains whose data distribution is
not i.i.d to the training domains. This failure often stems from learning
non-generalizable features in the training domains that are spuriously
correlated with the label of data. To address this shortcoming, there has been
a growing surge of interest in learning good explanations that are hard to
vary, which is studied under the notion of Out-of-Distribution (OOD)
Generalization. The search for good explanations that are \textit{invariant}
across different domains can be seen as finding local (global) minimas in the
loss landscape that hold true across all of the training domains. In this
paper, we propose a masking strategy, which determines a continuous weight
based on the agreement of gradients that flow in each edge of network, in order
to control the amount of update received by the edge in each step of
optimization. Particularly, our proposed technique referred to as "Smoothed-AND
(SAND)-masking", not only validates the agreement in the direction of gradients
but also promotes the agreement among their magnitudes to further ensure the
discovery of invariances across training domains. SAND-mask is validated over
the Domainbed benchmark for domain generalization and significantly improves
the state-of-the-art accuracy on the Colored MNIST dataset while providing
competitive results on other domain generalization datasets.
