# SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence Source: [SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:15:13.470Z Authors: Viktar Dubovik, {\\L}ukasz Struski, Jacek Tabor, Dawid Rymarczyk Published: 2025-07-25T14:34:15.000Z ## Abstract Understanding the decisions made by deep neural networks is essential in high-stakes domains such as medical imaging and autonomous driving. Yet, these models often lack transparency, particularly in computer vision. Prototypical-parts-based neural networks have emerged as a promising solution by offering concept-level explanations. However, most are limited to fine-grained classification tasks, with few exceptions such as InfoDisent. InfoDisent extends prototypical models to large-scale datasets like ImageNet, but produces complex explanations. We introduce Sparse Information Disentanglement for Explainability (SIDE), a novel method that improves the interpretability of prototypical parts through a dedicated training and pruning scheme that enforces sparsity. Combined with sigmoid activations in place of softmax, this approach allows SIDE to associate each class with only a small set of relevant prototypes. Extensive experiments show that SIDE matches the accuracy of existing methods while reducing explanation size by over $90\\%$, substantially enhancing the understandability of prototype-based explanations. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()