Analyzing Classifiers: Fisher Vectors and Deep Neural Networks

1 December 2015

Papers citing "Analyzing Classifiers: Fisher Vectors and Deep Neural Networks"

40 / 40 papers shown

Title
Investigating the Relationship Between Debiasing and Artifact Removal using Saliency Maps Lukasz Sztukiewicz Ignacy Stepka Michał Wiliński Jerzy Stefanowski 44 0 0 28 Feb 2025
Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognition Chong Wang Yuanhong Chen Fengbei Liu Yuyuan Liu Davis J. McCarthy Helen Frazer Gustavo Carneiro 29 1 0 30 Nov 2023
A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI) Timo Speith Markus Langer 29 12 0 26 Jul 2023
A Vulnerability of Attribution Methods Using Pre-Softmax Scores Miguel A. Lerma Mirtha Lucas FAtt 19 0 0 06 Jul 2023
On The Coherence of Quantitative Evaluation of Visual Explanations Benjamin Vandersmissen José Oramas XAI FAtt 36 3 0 14 Feb 2023
DORA: Exploring Outlier Representations in Deep Neural Networks Kirill Bykov Mayukh Deb Dennis Grinwald Klaus-Robert Muller Marina M.-C. Höhne 27 12 0 09 Jun 2022
[Reproducibility Report] Explainable Deep One-Class Classification João P C Bertoldo Etienne Decencière 23 0 0 06 Jun 2022
How explainable are adversarially-robust CNNs? Mehdi Nourelahi Lars Kotthoff Peijie Chen Anh Totti Nguyen AAML FAtt 22 8 0 25 May 2022
Interpretability of Machine Learning Methods Applied to Neuroimaging Elina Thibeau-Sutre S. Collin Ninon Burgos O. Colliot 16 4 0 14 Apr 2022
Explainable multiple abnormality classification of chest CT volumes R. Draelos Lawrence Carin MedIm 34 12 0 24 Nov 2021
Explaining Bayesian Neural Networks Kirill Bykov Marina M.-C. Höhne Adelaida Creosteanu Klaus-Robert Muller Frederick Klauschen Shinichi Nakajima Marius Kloft BDL AAML 34 25 0 23 Aug 2021
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy Christopher J. Anders David Neumann Wojciech Samek K. Müller Sebastian Lapuschkin 33 64 0 24 Jun 2021
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research Markus Langer Daniel Oster Timo Speith Holger Hermanns Lena Kästner Eva Schmidt Andreas Sesing Kevin Baum XAI 68 415 0 15 Feb 2021
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 24 63 0 18 Dec 2020
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations João Bento Pedro Saleiro André F. Cruz Mário A. T. Figueiredo P. Bizarro FAtt AI4TS 24 88 0 30 Nov 2020
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks Kirill Bykov Marina M.-C. Höhne Klaus-Robert Muller Shinichi Nakajima Marius Kloft UQCV FAtt 27 31 0 16 Jun 2020
Towards Interpretable Deep Learning Models for Knowledge Tracing Yu Lu De-Wu Wang Qinggang Meng Penghe Chen 17 36 0 13 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 41 371 0 30 Apr 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 44 82 0 17 Mar 2020
On the Similarity of Deep Learning Representations Across Didactic and Adversarial Examples P. Douglas F. Farahani AAML 19 5 0 17 Feb 2020
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis D. Slijepcevic Fabian Horst Sebastian Lapuschkin Anna-Maria Raberger Matthias Zeppelzauer Wojciech Samek C. Breiteneder W. Schöllhorn B. Horsak 36 50 0 16 Dec 2019
Towards Best Practice in Explaining Neural Network Decisions with LRP M. Kohlbrenner Alexander Bauer Shinichi Nakajima Alexander Binder Wojciech Samek Sebastian Lapuschkin 22 148 0 22 Oct 2019
Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly Supervised Instance Segmentation Weifeng Ge Sheng Guo Weilin Huang Matthew R. Scott 22 48 0 07 Oct 2019
Towards Explainable Artificial Intelligence Wojciech Samek K. Müller XAI 32 436 0 26 Sep 2019
Software and application patterns for explanation methods Maximilian Alber 33 11 0 09 Apr 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 17 996 0 26 Feb 2019
An Overview of Computational Approaches for Interpretation Analysis Philipp Blandfort Jörn Hees D. Patton 21 2 0 09 Nov 2018
Explaining the Unique Nature of Individual Gait Patterns with Deep Learning Fabian Horst Sebastian Lapuschkin Wojciech Samek K. Müller W. Schöllhorn AI4CE 28 207 0 13 Aug 2018
Layer-wise Relevance Propagation for Explainable Recommendations Homanga Bharadhwaj FAtt 21 8 0 17 Jul 2018
Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles Alexander Binder M. Bockmayr Miriam Hagele S. Wienert Daniel Heim ... M. Dietel A. Hocke C. Denkert K. Müller Frederick Klauschen AI4CE 12 27 0 28 May 2018
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning Weifeng Ge Sibei Yang Yizhou Yu 32 189 0 26 Feb 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation Wojciech Samek G. Montavon Alexander Binder Sebastian Lapuschkin K. Müller FAtt AI4CE 40 48 0 24 Nov 2016
VisualBackProp: efficient visualization of CNNs Mariusz Bojarski A. Choromańska K. Choromanski Bernhard Firner L. Jackel Urs Muller Karol Zieba FAtt 30 74 0 16 Nov 2016
Understanding intermediate layers using linear classifier probes Guillaume Alain Yoshua Bengio FAtt 50 894 0 05 Oct 2016
Optimistic and Pessimistic Neural Networks for Scene and Object Recognition René Grzeszick Sebastian Sudholt G. Fink UQCV 36 4 0 26 Sep 2016
Identifying individual facial expressions by deconstructing a neural network F. Arbabzadah G. Montavon K. Müller Wojciech Samek CVBM FAtt 30 31 0 23 Jun 2016
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers Alexander Binder G. Montavon Sebastian Lapuschkin K. Müller Wojciech Samek FAtt 22 453 0 04 Apr 2016
Controlling Explanatory Heatmap Resolution and Semantics via Decomposition Depth Sebastian Bach Alexander Binder K. Müller Wojciech Samek FAtt 26 24 0 21 Mar 2016
Evaluating the visualization of what a Deep Neural Network has learned Wojciech Samek Alexander Binder G. Montavon Sebastian Lapuschkin K. Müller XAI 74 1,180 0 21 Sep 2015