Papers citing 'Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning'

Title
Enhancing Pre-trained Representation Classifiability can Boost its InterpretabilityInternational Conference on Learning Representations (ICLR), 2025 Shufan Shen Zhaobo Qi Junshu Sun Qingming Huang Qi Tian Shuhui Wang FAtt 336 4 0 28 Oct 2025
NeurFlow: Interpreting Neural Networks through Neuron Groups and Functional InteractionsInternational Conference on Learning Representations (ICLR), 2025 Tue Cao Nhat X. Hoang Hieu H. Pham P. Nguyen My T. Thai 477 2 0 22 Feb 2025
A Neurosymbolic Framework for Bias Correction in CNNs Parth Padalkar Natalia Slusarz Ekaterina Komendantskaya Gopal Gupta 199 0 0 24 May 2024
Using Logic Programming and Kernel-Grouping for Improving Interpretability of Convolutional Neural Networks Parth Padalkar Gopal Gupta 132 3 0 19 Oct 2023
NeSyFOLD: Neurosymbolic Framework for Interpretable Image Classification Parth Padalkar Huaduo Wang Gopal Gupta 136 2 0 30 Jan 2023
AutoProtoNet: Interpretability for Prototypical Networks Pedro Sandoval Segura W. Lawson 73 2 0 02 Apr 2022
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIACM Computing Surveys (ACM CSUR), 2022 Meike Nauta Jan Trienes Shreyasi Pathak Elisa Nguyen Michelle Peters Yasmin Schmitt Jorg Schlotterer M. V. Keulen C. Seifert ELM XAI 487 541 0 20 Jan 2022
Finding Representative Interpretations on Convolutional Neural NetworksIEEE International Conference on Computer Vision (ICCV), 2021 P. C. Lam Lingyang Chu Maxim Torgonskiy Jian Pei Yong Zhang Lanjun Wang FAtt SSL HAI 135 7 0 13 Aug 2021
Explainability-aided Domain Generalization for Image Classification Robin M. Schmidt FAtt OOD 163 2 0 05 Apr 2021
Demystifying Deep Neural Networks Through Interpretation: A Survey Giang Dao Minwoo Lee FaML FAtt 176 1 0 13 Dec 2020
ERIC: Extracting Relations Inferred from ConvolutionsAsian Conference on Computer Vision (ACCV), 2020 Joe Townsend Theodoros Kasioumis Hiroya Inakoshi NAI FAtt 155 16 0 19 Oct 2020
What do CNN neurons learn: Visualization & Clustering Haoyue Dai SSL 88 0 0 18 Oct 2020
Explainability in Deep Reinforcement Learning Alexandre Heuillet Fabien Couthouis Natalia Díaz Rodríguez XAI 717 313 0 15 Aug 2020
Training Interpretable Convolutional Neural Networks by Differentiating Class-specific FiltersEuropean Conference on Computer Vision (ECCV), 2020 Haoyun Liang Zhihao Ouyang Yuyuan Zeng Hang Su Zihao He Shutao Xia Jun Zhu Bo Zhang 220 50 0 16 Jul 2020
Learning a functional control for high-frequency finance Laura Leal Mathieu Laurière Charles-Albert Lehalle AIFin 106 22 0 17 Jun 2020
Explainable Artificial Intelligence: a Systematic Review Giulia Vilone Luca Longo XAI 519 299 0 29 May 2020
Interpretable and Accurate Fine-grained Recognition via Region Grouping Zixuan Huang Yin Li 259 154 0 21 May 2020
Explainable Deep Learning: A Field Guide for the UninitiatedJournal of Artificial Intelligence Research (JAIR), 2020 Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 346 415 0 30 Apr 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIInformation Fusion (Inf. Fusion), 2019 Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 690 7,323 0 22 Oct 2019
A game method for improving the interpretability of convolution neural network Jinwei Zhao Qizhou Wang Fuqiang Zhang Wanli Qiu Yufei Wang Yu Liu Guo Xie Weigang Ma Bin Wang Xinhong Hei AI4CE 131 0 0 21 Oct 2019
Saliency Tubes: Visual Explanations for Spatio-Temporal Convolutions Alexandros Stergiou G. Kapidis Grigorios Kalliatakis C. Chrysoulas R. Veltkamp R. Poppe FAtt 145 50 0 04 Feb 2019
Interpretable CNNs for Object Classification Quanshi Zhang Xin Eric Wang Ying Nian Wu Huilin Zhou Song-Chun Zhu 132 61 0 08 Jan 2019
Explanatory Graphs for CNNs Quanshi Zhang Xin Eric Wang Ruiming Cao Ying Nian Wu Feng Shi Song-Chun Zhu FAtt GNN 94 3 0 18 Dec 2018
Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering Quanshi Zhang Ruiming Cao Ying Nian Wu Song-Chun Zhu 119 14 0 18 Dec 2018
Variational Saccading: Efficient Inference for Large Resolution Images Jason Ramapuram M. Diephuis Frantzeska Lavda Russ Webb Alexandros Kalousis 221 5 0 08 Dec 2018
Counterfactuals uncover the modular structure of deep generative models M. Besserve Arash Mehrjou Rémy Sun Bernhard Schölkopf DRL BDL DiffM 237 108 0 08 Dec 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 905 2,078 0 31 May 2018
Unsupervised Learning of Neural Networks to Explain Neural Networks Quanshi Zhang Yu Yang Yuchen Liu Ying Nian Wu Song-Chun Zhu FAtt SSL 143 28 0 18 May 2018
Visual Interpretability for Deep Learning: a Survey Quanshi Zhang Song-Chun Zhu FaML HAI 328 875 0 02 Feb 2018
Interpreting CNNs via Decision Trees Quanshi Zhang Yu Yang Ying Nian Wu Song-Chun Zhu FAtt 243 339 0 01 Feb 2018
Controllable Top-down Feature Transformer Zhiwei Jia Haoshen Hong Siyang Wang Kwonjoon Lee Zhuowen Tu ViT 174 2 0 06 Dec 2017
Examining CNN Representations with respect to Dataset BiasAAAI Conference on Artificial Intelligence (AAAI), 2017 Quanshi Zhang Wenguan Wang Song-Chun Zhu SSL FAtt 185 110 0 29 Oct 2017
Interpretable Convolutional Neural Networks Quanshi Zhang Ying Nian Wu Song-Chun Zhu FAtt 363 823 0 02 Oct 2017
Interpreting CNN Knowledge via an Explanatory Graph Quanshi Zhang Ruiming Cao Feng Shi Ying Nian Wu Song-Chun Zhu FAtt GNN SSL 190 251 0 05 Aug 2017
Interactively Transferring CNN Patterns for Part Localization Quanshi Zhang Ruiming Cao Shengming Zhang Mark Edmonds Ying Nian Wu Song-Chun Zhu 135 16 0 05 Aug 2017
Mining Object Parts from CNNs via Active Question-Answering Quanshi Zhang Ruiming Cao Ying Nian Wu Song-Chun Zhu 131 26 0 11 Apr 2017