Investigating the influence of noise and distractors on the interpretation of neural networks

22 November 2016

Pieter-Jan Kindermans

Papers citing "Investigating the influence of noise and distractors on the interpretation of neural networks"

31 / 31 papers shown

Title
Explanation Regularisation through the Lens of Attributions Pedro Ferreira Wilker Aziz Ivan Titov 48 1 0 23 Jul 2024
NeuralSentinel: Safeguarding Neural Network Reliability and Trustworthiness Xabier Echeberria-Barrio Mikel Gorricho Selene Valencia Francesco Zola AAML 26 1 0 12 Feb 2024
XAI-CLASS: Explanation-Enhanced Text Classification with Extremely Weak Supervision Daniel Hajialigol Hanwen Liu Xuan Wang VLM 21 5 0 31 Oct 2023
Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts Haochen Tan Han Wu Wei Shao Xinyun Zhang Mingjie Zhan Zhaohui Hou Ding Liang Linqi Song 47 0 0 13 May 2023
Guide the Learner: Controlling Product of Experts Debiasing Method Based on Token Attribution Similarities Ali Modarressi Hossein Amirkhani Mohammad Taher Pilehvar 29 2 0 06 Feb 2023
Quantitative Metrics for Evaluating Explanations of Video DeepFake Detectors Federico Baldassarre Quentin Debard Gonzalo Fiz Pontiveros Tri Kurniawan Wijaya 44 4 0 07 Oct 2022
Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media Yida Mu Pu Niu Nikolaos Aletras 34 12 0 21 Apr 2022
Backdooring Explainable Machine Learning Maximilian Noppel Lukas Peter Christian Wressnegger AAML 18 5 0 20 Apr 2022
Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers Zunlei Feng Jiacong Hu Sai Wu Xiaotian Yu Mingli Song Xiuming Zhang 45 13 0 09 Dec 2021
Improving Deep Learning Interpretability by Saliency Guided Training Aya Abdelsalam Ismail H. C. Bravo S. Feizi FAtt 25 80 0 29 Nov 2021
"How Does It Detect A Malicious App?" Explaining the Predictions of AI-based Android Malware Detector Zhi Lu V. Thing AAML 24 4 0 06 Nov 2021
Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience G. Chrysostomou Nikolaos Aletras 32 16 0 31 Aug 2021
CAMERAS: Enhanced Resolution And Sanity preserving Class Activation Mapping for image saliency M. Jalwana Naveed Akhtar Bennamoun Ajmal Mian 30 54 0 20 Jun 2021
CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding Dustin Wright Isabelle Augenstein 16 24 0 23 May 2021
Improving the Faithfulness of Attention-based Explanations with Task-specific Information for Text Classification G. Chrysostomou Nikolaos Aletras 27 37 0 06 May 2021
Flexible Instance-Specific Rationalization of NLP Models G. Chrysostomou Nikolaos Aletras 31 14 0 16 Apr 2021
On the Impact of Interpretability Methods in Active Image Augmentation Method F. Santos Cleber Zanchettin L. Matos P. Novais AAML 33 2 0 24 Feb 2021
A Diagnostic Study of Explainability Techniques for Text Classification Pepa Atanasova J. Simonsen Christina Lioma Isabelle Augenstein XAI FAtt 17 220 0 25 Sep 2020
Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs Ruigang Fu Qingyong Hu Xiaohu Dong Yulan Guo Yinghui Gao Biao Li FAtt 24 266 0 05 Aug 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 43 371 0 30 Apr 2020
What went wrong and when? Instance-wise Feature Importance for Time-series Models S. Tonekaboni Shalmali Joshi Kieran Campbell David Duvenaud Anna Goldenberg FAtt OOD AI4TS 53 14 0 05 Mar 2020
When Explanations Lie: Why Many Modified BP Attributions Fail Leon Sixt Maximilian Granz Tim Landgraf BDL FAtt XAI 13 132 0 20 Dec 2019
Software and application patterns for explanation methods Maximilian Alber 38 11 0 09 Apr 2019
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation Sahil Singla Eric Wallace Shi Feng S. Feizi FAtt 31 59 0 01 Feb 2019
ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU William Caicedo-Torres Jairo Gutiérrez 14 78 0 24 Jan 2019
Understanding Individual Decisions of CNNs via Contrastive Backpropagation Jindong Gu Yinchong Yang Volker Tresp FAtt 17 94 0 05 Dec 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges Gabrielle Ras Marcel van Gerven W. Haselager XAI 17 217 0 20 Mar 2018
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan MLT FAtt 44 561 0 21 Feb 2018
Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos Hugo Jair Escalante Heysem Kaya A. A. Salah Sergio Escalera Yağmur Güçlütürk ... Furkan Gürpinar Achmadnoer Sukma Wicaksana Cynthia C. S. Liem Marcel van Gerven R. Lier 25 61 0 02 Feb 2018
The (Un)reliability of saliency methods Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne D. Erhan Been Kim FAtt XAI 45 679 0 02 Nov 2017
Learning how to explain neural networks: PatternNet and PatternAttribution Pieter-Jan Kindermans Kristof T. Schütt Maximilian Alber K. Müller D. Erhan Been Kim Sven Dähne XAI FAtt 27 338 0 16 May 2017