Batch Normalization (BN) and its variants have
delivered tremendous success in combating the covariate shift induced
by the training step of deep learning methods. While these techniques
normalize feature distributions by standardizing with batch
statistics, they do not correct the influence on features from
extraneous variables or multiple distributions. Such extra variables,
referred to as metadata here, may create bias or confounding effects
(e.g., race when classifying gender from face images). We introduce
the Metadata Normalization (MDN) layer, a new batch-level operation
which can be used end-to-end within the training framework, to correct
the influence of metadata on feature distributions. MDN adopts a
regression analysis technique traditionally used for preprocessing to
remove (regress out) the metadata effects on model features during
training. We utilize a metric based on distance correlation to
quantify the distribution bias from the metadata and demonstrate that
our method successfully removes metadata effects on four diverse
settings: one synthetic, one 2D image, one video, and one 3D medical
image dataset.
