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LIMEcraft: Handcrafted superpixel selection and inspection for Visual eXplanations

15 November 2021

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Papers citing "LIMEcraft: Handcrafted superpixel selection and inspection for Visual eXplanations"

21 / 21 papers shown

Title
Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies Weronika Hryniewska Przemysław Bombiński P. Szatkowski Paulina Tomaszewska A. Przelaskowski P. Biecek OOD 85 47 0 11 Dec 2020
A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanation Sheng Shi Xinfeng Zhang Wei Fan FAtt 50 28 0 18 Feb 2020
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks Q. Huang M. Yamada Yuan Tian Dinesh Singh Dawei Yin Yi-Ju Chang FAtt 100 359 0 17 Jan 2020
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 81 822 0 06 Nov 2019
A study of data and label shift in the LIME framework Amir Hossein Akhavan Rahnama Henrik Bostrom FAtt 33 21 0 31 Oct 2019
Effect of Superpixel Aggregation on Explanations in LIME -- A Case Study with Biological Data Ludwig Schallner Johannes Rabold O. Scholz Ute Schmid FAtt 93 21 0 17 Oct 2019
Enriching Visual with Verbal Explanations for Relational Concepts -- Combining LIME with Aleph Johannes Rabold Hannah Deininger M. Siebers Ute Schmid FAtt 53 57 0 04 Oct 2019
NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks Isaac Ahern Adam Noack Luis Guzman-Nateras Dejing Dou Boyang Albert Li Jun Huan FAtt 32 39 0 10 Sep 2019
ALIME: Autoencoder Based Approach for Local Interpretability Sharath M. Shankaranarayana D. Runje FAtt 53 104 0 04 Sep 2019
DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems Muhammad Rehman Zafar N. Khan FAtt 114 158 0 24 Jun 2019
Local Interpretable Model-agnostic Explanations of Bayesian Predictive Models via Kullback-Leibler Projections Tomi Peltola FAtt BDL 54 40 0 05 Oct 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 89 528 0 21 Jun 2018
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 188 1,176 0 19 Jun 2018
Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP) Linwei Hu Jie Chen V. Nair Agus Sudjianto FAtt 57 63 0 02 Jun 2018
Visualizing and Understanding Atari Agents S. Greydanus Anurag Koul Jonathan Dodge Alan Fern FAtt 114 348 0 31 Oct 2017
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks Aditya Chattopadhyay Anirban Sarkar Prantik Howlader V. Balasubramanian FAtt 119 2,311 0 30 Oct 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 83 1,525 0 11 Apr 2017
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis L. Zintgraf Taco S. Cohen T. Adel Max Welling FAtt 147 709 0 15 Feb 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 335 20,110 0 07 Oct 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,071 0 16 Feb 2016
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 603 15,907 0 12 Nov 2013