Interpret the Predictions of Deep Networks via Re-Label Distillation

Interpret the Predictions of Deep Networks via Re-Label Distillation

20 September 2024

Shiming Ge

Daichi Zhang

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Papers citing "Interpret the Predictions of Deep Networks via Re-Label Distillation"

18 / 18 papers shown

Title
A Survey on Neural Network Interpretability Yu Zhang Peter Tiño A. Leonardis K. Tang FaML XAI 204 682 0 28 Dec 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 87 403 0 19 Oct 2020
Explaining Neural Network Predictions for Functional Data Using Principal Component Analysis and Feature Importance Katherine Goode Daniel Ries J. Zollweg 23 3 0 15 Oct 2020
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping Cher Bass Mariana da Silva Carole Sudre Petru-Daniel Tudosiu Stephen M. Smith E. C. Robinson FAtt 40 40 0 15 Jun 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI 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 124 6,269 0 22 Oct 2019
Understanding Deep Networks via Extremal Perturbations and Smooth Masks Ruth C. Fong Mandela Patrick Andrea Vedaldi AAML 73 416 0 18 Oct 2019
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks Mehdi Neshat Zifan Wang Bradley Alexander Fan Yang Zijian Zhang Sirui Ding Markus Wagner Xia Hu FAtt 93 1,074 0 03 Oct 2019
Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks Jörg Wagner Jan M. Köhler Tobias Gindele Leon Hetzel Thaddäus Wiedemer Sven Behnke AAML FAtt 83 122 0 07 Aug 2019
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation Xuan Liu Xiaoguang Wang Stan Matwin HAI 57 101 0 28 Dec 2018
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 181 1,171 0 19 Jun 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges Gabrielle Ras Marcel van Gerven W. Haselager XAI 102 219 0 20 Mar 2018
Visual Interpretability for Deep Learning: a Survey Quanshi Zhang Song-Chun Zhu FaML HAI 142 821 0 02 Feb 2018
Grammar Variational Autoencoder Matt J. Kusner Brooks Paige José Miguel Hernández-Lobato BDL DRL 85 844 0 06 Mar 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 321 20,070 0 07 Oct 2016
Top-down Neural Attention by Excitation Backprop Jianming Zhang Zhe Lin Jonathan Brandt Xiaohui Shen Stan Sclaroff 89 948 0 01 Aug 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,027 0 16 Feb 2016
Distilling the Knowledge in a Neural Network Geoffrey E. Hinton Oriol Vinyals J. Dean FedML 362 19,723 0 09 Mar 2015
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 595 15,893 0 12 Nov 2013