Prototype-Based Explanation

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11 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Engram Memory Network: Brain-Inspired Prototype Explanations
Oct 20, 2026Hyunjun Kim, Myoung Hoon HaPrototype-Based Explanation Explainable Image Classification
Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea · Center for Neuroscience-inspired AI, KAIST, Daejeon, South Korea
PiPS: Post-Hoc Prototypical Explanations for Interpretable Semantic Segmentation
Sep 15, 2026Miłosz Adamczyk, Tymoteusz Zapala, Piotr Borycki +1Explainable Artificial Intelligence Prototype-Based Explanation
Jagiellonian University · Wrocław University of Science and Technology · IDEAS Research Institute
Beyond $L_2$ : Generalizing Abductive Latent Explanations to Diverse Prototype-Based Architectures
Aug 17, 2026Jules Soria, Alban Grastien, Romain Xu-Darme +3Explainable Artificial Intelligence Neural Network Interpretability
Université Paris-Saclay, CEA, List, F-91120, Palaiseau, France
Beyond Points: Spherical Distributional Part Prototypes for Interpretable Classification
Jun 25, 2026Duarte Leão, Diogo Pereira Araújo, Catarina Barata +1Prototype-Based Classification Image Classification
Instituto Superior Técnico, Universidade de Lisboa · Carnegie Mellon University
Alike Parts: A Feature-Informed Approach to Local and Global Prototype Explanations
May 20, 2026Jacek Karolczak, Jerzy StefanowskiPrototype-Based Classification Prototype-Based Explanation
Institute of Computing Science Poznan University of Technology ul. Piotrowo 2, 60-965 Pozna´n, Poland
APEX: Audio Prototype EXplanations for Classification Tasks
May 11, 2026Piotr Kawa, Kornel Howil, Piotr Borycki +3Explainable Artificial Intelligence Prototype-Based Explanation
Department of Artificial Intelligence, Wroclaw University of Science and Technology, Poland · IDEAS Research Institute, Poland · Faculty of Mathematics and Computer Science, Jagiellonian University, Poland +1
ProDG: Prototypes for Data-Free Generative Post-Hoc Explainability
May 9, 2026Piotr Borycki, Magdalena Trędowicz, Jacek Tabor +2Explainable Artificial Intelligence Prototype-Based Explanation
Jagiellonian University · IDEAS Research Institute · Centre for Credible AI +1
Prototype-Grounded Concept Models for Verifiable Concept Alignment
Apr 17, 2026Stefano Colamonaco, David Debot, Pietro Barbiero +1Concept Bottleneck Models Concept Alignment
Department of Computer Science, KU Leuven · IBM Research, Zurich
Post-hoc Self-explanation of CNNs
Mar 30, 2026Ahcène Boubekki, Line H. ClemmensenPrototype-Based Explanation Neural Network Interpretability
Department of Mathematical Sciences, University of Copenhagen, Denmark
Principal Prototype Analysis on Manifold for Interpretable Reinforcement Learning
Mar 30, 2026Bodla Krishna Vamshi, Haizhao YangPrototype-Based Explanation Interpretability in RL
Department of Computer science, University of Maryland College park, USA · Department of Mathematics, University of Maryland College park, USA
Why Does It Look There? Structured Explanations for Image Classification
Mar 10, 2026Jiarui Li, Zixiang Yin, Samuel J Landry +2Prototype-Based Explanation Explainable Image Classification
Department of Computer Science, Tulane University, New Orleans LA 70118, USA · Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans LA 70112, USA

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Engram Memory Network: Brain-Inspired Prototype Explanations

Hyunjun Kim, Myoung Hoon Ha

Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea · Center for Neuroscience-inspired AI, KAIST, Daejeon, South Korea

Prototype-Based Explanation Explainable Image Classification Prototype-Based Classification Memory-Augmented Neural Networks Explainability Evaluation

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Artificial intelligence systems are increasingly deployed in high-stakes decision-making. In response, prototype-based models in explainable AI have attracted growing attention for their ability to retrieve visually similar examples as faithful explanations. Although these models achieve competitive classification accuracy, they often exhibit degraded explanation quality across datasets with varying characteristics, limiting their practical applicability. We draw on insights from cognitive neuroscience, particularly the human memory system, which encodes input-relevant information into representations that support recognition. We propose an Engram Memory Network (EMN), a model that emulates the ventral visual stream and inferior temporal cortex in the human brain. Central to this model is a Hopfield-inspired memory module that iteratively retrieves representative prototypes. These memory-refined latents are used for both reconstruction and classification, encouraging the selected prototypes to capture input-relevant information across diverse datasets. To address the lack of standardized evaluation for prototype explanations, we assess three complementary aspects: perceptual similarity (LPIPS and DISTS) for perceived resemblance, subclass alignment, measuring whether prototypes match the input's fine-grained category beyond supervised labels, and information preservation (NMI), measuring how much input information is captured by selected prototypes. Experiments show that EMN improves visual similarity by 17.06%, subclass alignment by 104.57%, and NMI by 42.42% on average across CIFAR-10/100, MNIST, HAM10000, and CUB-200, while achieving the highest average classification accuracy (87.62%) among all methods. These findings underscore the effectiveness of neuroscience-inspired explanation mechanisms in advancing the interpretability and reliability of prototype-based models.