Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability

20 July 2022

Papers citing "Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability"

7 / 7 papers shown

Title
Explanation Bottleneck Models Shinýa Yamaguchi Kosuke Nishida LRM BDL 51 1 0 26 Sep 2024
Embracing Diversity: Interpretable Zero-shot classification beyond one vector per class Mazda Moayeri Michael G. Rabbat Mark Ibrahim Diane Bouchacourt VLM 52 1 0 25 Apr 2024
Coarse-to-Fine Concept Bottleneck Models Konstantinos P. Panousis Dino Ienco Diego Marcos 28 5 0 03 Oct 2023
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
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 122 297 0 17 Oct 2019
Semantic Understanding of Scenes through the ADE20K Dataset Bolei Zhou Hang Zhao Xavier Puig Tete Xiao Sanja Fidler Adela Barriuso Antonio Torralba SSeg 253 1,829 0 18 Aug 2016
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 296 39,217 0 01 Sep 2014