  Notable progress has been made in numerous fields of machine learning based
on neural network-driven mutual information (MI) bounds. However, utilizing the
conventional MI-based losses is often challenging due to their practical and
mathematical limitations. In this work, we first identify the symptoms behind
their instability: (1) the neural network not converging even after the loss
seemed to converge, and (2) saturating neural network outputs causing the loss
to diverge. We mitigate both issues by adding a novel regularization term to
the existing losses. We theoretically and experimentally demonstrate that added
regularization stabilizes training. Finally, we present a novel benchmark that
evaluates MI-based losses on both the MI estimation power and its capability on
the downstream tasks, closely following the pre-existing supervised and
contrastive learning settings. We evaluate six different MI-based losses and
their regularized counterparts on multiple benchmarks to show that our approach
is simple yet effective.
