  Owing to their remarkable learning (and relearning) capabilities, deep neural
networks (DNNs) find use in numerous real-world applications. However, the
learning of these data-driven machine learning models is generally as good as
the data available to them for training. Hence, training datasets with
long-tail distribution pose a challenge for DNNs, since the DNNs trained on
them may provide a varying degree of classification performance across
different output classes. While the overall bias of such networks is already
highlighted in existing works, this work identifies the node bias that leads to
a varying sensitivity of the nodes for different output classes. To the best of
our knowledge, this is the first work highlighting this unique challenge in
DNNs, discussing its probable causes, and providing open challenges for this
new research direction. We support our reasoning using an empirical case study
of the networks trained on a real-world dataset.
