SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence

• URL: http://arxiv.org/abs/2507.19321v1
• Date: Fri, 25 Jul 2025 14:34:15 GMT
• Title: SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence
• Authors: Viktar Dubovik, Łukasz Struski, Jacek Tabor, Dawid Rymarczyk,
• Abstract summary: Prototypical-parts-based neural networks have emerged as a promising solution by offering concept-level explanations.<n>We introduce Sparse Information Disentanglement for Explainability (SIDE), a novel method that improves the interpretability of prototypical parts.
• Score: 9.975642488603937
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Beyond Patches: Mining Interpretable Part-Prototypes for Explainable AI [10.687381287384524]
  Part-prototypical concept mining network (PCMNet) is proposed to learn interpretable prototypes from meaningful regions.<n>PCMNet clusters prototypes into concept groups, creating semantically grounded explanations without requiring additional annotations.<n>Our experiments show that PCMNet can provide a high level of interpretability, stability, and robustness under clean and occluded scenarios.
  arXiv  Detail & Related papers  (2025-04-16T15:48:21Z)
• Interpretable Image Classification via Non-parametric Part Prototype Learning [14.390730075612248]
  Classifying images with an interpretable decision-making process is a long-standing problem in computer vision.<n>In recent years, Prototypical Part Networks has gained traction as an approach for self-explainable neural networks.<n>We present a framework for part-based interpretable image classification that learns a set of semantically distinctive object parts for each class.
  arXiv  Detail & Related papers  (2025-03-13T10:46:53Z)
• InfoDisent: Explainability of Image Classification Models by Information Disentanglement [10.89767277352967]
  We introduce InfoDisent, a hybrid approach to explainability based on the information bottleneck principle.<n>We demonstrate the effectiveness of InfoDisent through computational experiments and user studies across various datasets.
  arXiv  Detail & Related papers  (2024-09-16T14:39:15Z)
• Towards Scalable and Versatile Weight Space Learning [51.78426981947659]
  This paper introduces the SANE approach to weight-space learning. Our method extends the idea of hyper-representations towards sequential processing of subsets of neural network weights.
  arXiv  Detail & Related papers  (2024-06-14T13:12:07Z)
• Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective [64.04617968947697]
  We introduce a novel data-model co-design perspective: to promote superior weight sparsity. Specifically, customized Visual Prompts are mounted to upgrade neural Network sparsification in our proposed VPNs framework.
  arXiv  Detail & Related papers  (2023-12-03T13:50:24Z)
• Visual Recognition with Deep Nearest Centroids [57.35144702563746]
  We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective network for large-scale visual recognition. Compared with parametric counterparts, DNC performs better on image classification (CIFAR-10, ImageNet) and greatly boots pixel recognition (ADE20K, Cityscapes)
  arXiv  Detail & Related papers  (2022-09-15T15:47:31Z)
• A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification [9.365794791156972]
  This paper introduces a new Lightweight, Efficient and eXplainable-by-design convolutional neural network (LEXNet) for Internet traffic classification. LEXNet relies on a new residual block (for lightweight and efficiency purposes) and prototype layer (for explainability) Based on a commercial-grade dataset, our evaluation shows that LEXNet succeeds to maintain the same accuracy as the best performing state-of-the-art neural network.
  arXiv  Detail & Related papers  (2022-02-11T10:21:34Z)
• This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation [17.485732906337507]
  We present a case study of the self-explaining network, ProtoPNet, in the presence of a spectrum of artifacts. We introduce a novel method for generating more precise model-aware explanations. In order to obtain a clean dataset, we propose to use multi-view clustering strategies for segregating the artifact images.
  arXiv  Detail & Related papers  (2021-08-27T09:55:53Z)
• Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation [51.29486247405601]
  We design a framework to generate counterfactuals for raw data instances with the proposed Attribute-Informed Perturbation (AIP) By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework.
  arXiv  Detail & Related papers  (2021-01-18T08:37:13Z)
• Simplifying the explanation of deep neural networks with sufficient and necessary feature-sets: case of text classification [0.0]
  Deep neural networks (DNN) have demonstrated impressive performances solving a wide range of problems in various domains such as medicine, finance, law, etc. Despite their great performances, they have long been considered as black-box systems, providing good results without being able to explain them. This article proposes a method to simplify the prediction explanation of One-Dimensional (1D) Convolutional Neural Networks (CNN) by identifying sufficient and necessary features-sets.
  arXiv  Detail & Related papers  (2020-10-08T02:01:21Z)
• Closed-Form Factorization of Latent Semantics in GANs [65.42778970898534]
  A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images. In this work, we examine the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner. We propose a closed-form factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights.
  arXiv  Detail & Related papers  (2020-07-13T18:05:36Z)
• Prototypical Contrastive Learning of Unsupervised Representations [171.3046900127166]
  Prototypical Contrastive Learning (PCL) is an unsupervised representation learning method. PCL implicitly encodes semantic structures of the data into the learned embedding space. PCL outperforms state-of-the-art instance-wise contrastive learning methods on multiple benchmarks.
  arXiv  Detail & Related papers  (2020-05-11T09:53:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.