  Regularization is a fundamental technique to prevent over-fitting and to
improve generalization performances by constraining a model's complexity.
Current Deep Networks heavily rely on regularizers such as Data-Augmentation
(DA) or weight-decay, and employ structural risk minimization, i.e.
cross-validation, to select the optimal regularization hyper-parameters. In
this study, we demonstrate that techniques such as DA or weight decay produce a
model with a reduced complexity that is unfair across classes. The optimal
amount of DA or weight decay found from cross-validation leads to disastrous
model performances on some classes e.g. on Imagenet with a resnet50, the "barn
spider" classification test accuracy falls from $68\%$ to $46\%$ only by
introducing random crop DA during training. Even more surprising, such
performance drop also appears when introducing uninformative regularization
techniques such as weight decay. Those results demonstrate that our search for
ever increasing generalization performance -- averaged over all classes and
samples -- has left us with models and regularizers that silently sacrifice
performances on some classes. This scenario can become dangerous when deploying
a model on downstream tasks e.g. an Imagenet pre-trained resnet50 deployed on
INaturalist sees its performances fall from $70\%$ to $30\%$ on class \#8889
when introducing random crop DA during the Imagenet pre-training phase. Those
results demonstrate that designing novel regularizers without class-dependent
bias remains an open research question.
