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Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning

v1v2 (latest)

Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning

14 November 2016

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

36 / 36 papers shown

Enhancing Pre-trained Representation Classifiability can Boost its Interpretability

Enhancing Pre-trained Representation Classifiability can Boost its InterpretabilityInternational Conference on Learning Representations (ICLR), 2025

414

4

0

28 Oct 2025

NeurFlow: Interpreting Neural Networks through Neuron Groups and Functional Interactions

NeurFlow: Interpreting Neural Networks through Neuron Groups and Functional InteractionsInternational Conference on Learning Representations (ICLR), 2025

531

2

0

22 Feb 2025

A Neurosymbolic Framework for Bias Correction in CNNs

A Neurosymbolic Framework for Bias Correction in CNNs

Natalia Slusarz

Ekaterina Komendantskaya

267

0

0

24 May 2024

Using Logic Programming and Kernel-Grouping for Improving
Interpretability of Convolutional Neural Networks

Using Logic Programming and Kernel-Grouping for Improving Interpretability of Convolutional Neural Networks

148

3

0

19 Oct 2023

NeSyFOLD: Neurosymbolic Framework for Interpretable Image Classification

NeSyFOLD: Neurosymbolic Framework for Interpretable Image Classification

164

2

0

30 Jan 2023

AutoProtoNet: Interpretability for Prototypical Networks

AutoProtoNet: Interpretability for Prototypical Networks

Pedro Sandoval Segura

81

2

0

02 Apr 2022

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic
Review on Evaluating Explainable AI

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIACM Computing Surveys (ACM CSUR), 2022

Shreyasi Pathak

Michelle Peters

Jorg Schlotterer

602

561

0

20 Jan 2022

Finding Representative Interpretations on Convolutional Neural Networks

Finding Representative Interpretations on Convolutional Neural NetworksIEEE International Conference on Computer Vision (ICCV), 2021

Maxim Torgonskiy

Yong Zhang

175

7

0

13 Aug 2021

Explainability-aided Domain Generalization for Image Classification

Explainability-aided Domain Generalization for Image Classification

Robin M. Schmidt

193

2

0

05 Apr 2021

Demystifying Deep Neural Networks Through Interpretation: A Survey

Demystifying Deep Neural Networks Through Interpretation: A Survey

244

1

0

13 Dec 2020

ERIC: Extracting Relations Inferred from Convolutions

ERIC: Extracting Relations Inferred from ConvolutionsAsian Conference on Computer Vision (ACCV), 2020

Theodoros Kasioumis

Hiroya Inakoshi

159

16

0

19 Oct 2020

What do CNN neurons learn: Visualization & Clustering

What do CNN neurons learn: Visualization & Clustering

100

0

0

18 Oct 2020

Explainability in Deep Reinforcement Learning

Explainability in Deep Reinforcement Learning

Alexandre Heuillet

Fabien Couthouis

Natalia Díaz Rodríguez

848

317

0

15 Aug 2020

Training Interpretable Convolutional Neural Networks by Differentiating
Class-specific Filters

Training Interpretable Convolutional Neural Networks by Differentiating Class-specific FiltersEuropean Conference on Computer Vision (ECCV), 2020

Hang Su

Jun Zhu

288

50

0

16 Jul 2020

Learning a functional control for high-frequency finance

Learning a functional control for high-frequency finance

Mathieu Laurière

Charles-Albert Lehalle

122

22

0

17 Jun 2020

Explainable Artificial Intelligence: a Systematic Review

Explainable Artificial Intelligence: a Systematic Review

598

300

0

29 May 2020

Interpretable and Accurate Fine-grained Recognition via Region Grouping

Interpretable and Accurate Fine-grained Recognition via Region Grouping

Zixuan Huang

Yin Li

283

156

0

21 May 2020

Explainable Deep Learning: A Field Guide for the Uninitiated

Explainable Deep Learning: A Field Guide for the UninitiatedJournal of Artificial Intelligence Research (JAIR), 2020

Marcel van Gerven

433

417

0

30 Apr 2020

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies,
Opportunities and Challenges toward Responsible AI

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIInformation Fusion (Inf. Fusion), 2019

Alejandro Barredo Arrieta

Natalia Díaz Rodríguez

Adrien Bennetot

...

Richard Benjamins

Francisco Herrera

877

7,501

0

22 Oct 2019

A game method for improving the interpretability of convolution neural
network

A game method for improving the interpretability of convolution neural network

Fuqiang Zhang

Yufei Wang

144

0

0

21 Oct 2019

Saliency Tubes: Visual Explanations for Spatio-Temporal Convolutions

Saliency Tubes: Visual Explanations for Spatio-Temporal Convolutions

Alexandros Stergiou

Grigorios Kalliatakis

166

50

0

04 Feb 2019

Interpretable CNNs for Object Classification

Interpretable CNNs for Object Classification

142

62

0

08 Jan 2019

Explanatory Graphs for CNNs

Explanatory Graphs for CNNs

110

3

0

18 Dec 2018

Mining Interpretable AOG Representations from Convolutional Networks via
Active Question Answering

Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering

123

14

0

18 Dec 2018

Variational Saccading: Efficient Inference for Large Resolution Images

Variational Saccading: Efficient Inference for Large Resolution Images

Jason Ramapuram

Frantzeska Lavda

Alexandros Kalousis

254

5

0

08 Dec 2018

Counterfactuals uncover the modular structure of deep generative models

Counterfactuals uncover the modular structure of deep generative models

Bernhard Schölkopf

259

108

0

08 Dec 2018

Explaining Explanations: An Overview of Interpretability of Machine
Learning

Explaining Explanations: An Overview of Interpretability of Machine Learning

Leilani H. Gilpin

Michael A. Specter

1.1K

2,112

0

31 May 2018

Unsupervised Learning of Neural Networks to Explain Neural Networks

Unsupervised Learning of Neural Networks to Explain Neural Networks

167

27

0

18 May 2018

Visual Interpretability for Deep Learning: a Survey

Visual Interpretability for Deep Learning: a Survey

360

879

0

02 Feb 2018

Interpreting CNNs via Decision Trees

Interpreting CNNs via Decision Trees

271

342

0

01 Feb 2018

Controllable Top-down Feature Transformer

Controllable Top-down Feature Transformer

198

2

0

06 Dec 2017

Examining CNN Representations with respect to Dataset Bias

Examining CNN Representations with respect to Dataset BiasAAAI Conference on Artificial Intelligence (AAAI), 2017

202

110

0

29 Oct 2017

Interpretable Convolutional Neural Networks

Interpretable Convolutional Neural Networks

399

829

0

02 Oct 2017

Interpreting CNN Knowledge via an Explanatory Graph

Interpreting CNN Knowledge via an Explanatory Graph

215

252

0

05 Aug 2017

Interactively Transferring CNN Patterns for Part Localization

Interactively Transferring CNN Patterns for Part Localization

Shengming Zhang

144

16

0

05 Aug 2017

Mining Object Parts from CNNs via Active Question-Answering

Mining Object Parts from CNNs via Active Question-Answering

164

26

0

11 Apr 2017