  Despite impressive performance as evaluated on i.i.d. holdout data, deep
neural networks depend heavily on superficial statistics of the training data
and are liable to break under distribution shift. For example, subtle changes
to the background or texture of an image can break a seemingly powerful
classifier. Building on previous work on domain generalization, we hope to
produce a classifier that will generalize to previously unseen domains, even
when domain identifiers are not available during training. This setting is
challenging because the model may extract many distribution-specific
(superficial) signals together with distribution-agnostic (semantic) signals.
To overcome this challenge, we incorporate the gray-level co-occurrence matrix
(GLCM) to extract patterns that our prior knowledge suggests are superficial:
they are sensitive to the texture but unable to capture the gestalt of an
image. Then we introduce two techniques for improving our networks'
out-of-sample performance. The first method is built on the reverse gradient
method that pushes our model to learn representations from which the GLCM
representation is not predictable. The second method is built on the
independence introduced by projecting the model's representation onto the
subspace orthogonal to GLCM representation's. We test our method on the battery
of standard domain generalization data sets and, interestingly, achieve
comparable or better performance as compared to other domain generalization
methods that explicitly require samples from the target distribution for
training.
