This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks

5 May 2021

Papers citing "This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks"

9 / 9 papers shown

Title
MERA: Multimodal and Multiscale Self-Explanatory Model with Considerably Reduced Annotation for Lung Nodule Diagnosis Jiahao Lu Chong Yin Silvia Ingala Kenny Erleben M. Nielsen S. Darkner 51 0 0 27 Apr 2025
A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations S. Saralajew Ashish Rana T. Villmann Ammar Shaker OOD 87 1 0 20 Dec 2024
ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation [Technical Report] Hamed Ayoobi Nico Potyka Francesca Toni 36 2 0 26 Nov 2023
Take 5: Interpretable Image Classification with a Handful of Features Thomas Norrenbrock Marco Rudolph Bodo Rosenhahn FAtt 40 7 0 23 Mar 2023
ICICLE: Interpretable Class Incremental Continual Learning Dawid Rymarczyk Joost van de Weijer Bartosz Zieliñski Bartlomiej Twardowski CLL 32 28 0 14 Mar 2023
GlanceNets: Interpretabile, Leak-proof Concept-based Models Emanuele Marconato Andrea Passerini Stefano Teso 106 64 0 31 May 2022
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 66 114 0 06 Dec 2021
Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods Zohaib Salahuddin Henry C. Woodruff A. Chatterjee Philippe Lambin 18 302 0 01 Nov 2021
Toward a Unified Framework for Debugging Concept-based Models A. Bontempelli Fausto Giunchiglia Andrea Passerini Stefano Teso 20 4 0 23 Sep 2021