Combating the Instability of Mutual Information-based Losses via Regularization
This addresses a practical problem for researchers and practitioners using mutual information-based methods in fields like supervised and contrastive learning, though it is incremental as it builds on existing losses.
The paper tackled the instability of mutual information-based losses in machine learning by identifying symptoms like non-convergence and divergence, and mitigated these issues by adding a novel regularization term, showing that it stabilizes training both theoretically and experimentally.
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.