Improving the Interpretability of Deep Neural Networks with Knowledge Distillation

28 December 2018

Xuan Liu

Papers citing "Improving the Interpretability of Deep Neural Networks with Knowledge Distillation"

22 / 22 papers shown

Title
Interpret the Predictions of Deep Networks via Re-Label Distillation Yingying Hua Shiming Ge Daichi Zhang FAtt 40 0 0 20 Sep 2024
Knowledge Distillation of Convolutional Neural Networks through Feature Map Transformation using Decision Trees Maddimsetti Srinivas Debdoot Sheet FAtt 37 0 0 10 Mar 2024
Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation Ruitao Xie Jingbang Chen Limai Jiang Ru Xiao Yi-Lun Pan Yunpeng Cai GAN MedIm 27 0 0 12 Jun 2023
Using Knowledge Distillation to improve interpretable models in a retail banking context Maxime Biehler Mohamed Guermazi Célim Starck 62 2 0 30 Sep 2022
Towards Explaining Autonomy with Verbalised Decision Tree States K. Gavriilidis A. Munafò Helen F. Hastie Conlan Cesar M. Defilippo M. Benjamin 19 2 0 28 Sep 2022
Variance Tolerance Factors For Interpreting ALL Neural Networks Sichao Li Amanda S. Barnard FAtt 32 3 0 28 Sep 2022
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems Arne Gevaert Jonathan Peck Yvan Saeys 36 1 0 07 Sep 2022
Causality-Inspired Taxonomy for Explainable Artificial Intelligence Pedro C. Neto Tiago B. Gonccalves João Ribeiro Pinto W. Silva Ana F. Sequeira Arun Ross Jaime S. Cardoso XAI 43 12 0 19 Aug 2022
Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs Benjamin Fuhrer Yuval Shpigelman Chen Tessler Shie Mannor Gal Chechik E. Zahavi Gal Dalal 33 4 0 05 Jul 2022
Evaluating Feature Attribution Methods in the Image Domain Arne Gevaert Axel-Jan Rousseau Thijs Becker D. Valkenborg T. D. Bie Yvan Saeys FAtt 27 22 0 22 Feb 2022
Deeply Explain CNN via Hierarchical Decomposition Mingg-Ming Cheng Peng-Tao Jiang Linghao Han Liang Wang Philip Torr FAtt 53 15 0 23 Jan 2022
Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations Siddhant Arora Danish Pruthi Norman M. Sadeh William W. Cohen Zachary Chase Lipton Graham Neubig FAtt 40 38 0 17 Dec 2021
A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks T. Dash Sharad Chitlangia Aditya Ahuja A. Srinivasan 40 127 0 21 Jul 2021
On Guaranteed Optimal Robust Explanations for NLP Models Emanuele La Malfa A. Zbrzezny Rhiannon Michelmore Nicola Paoletti Marta Z. Kwiatkowska FAtt 19 47 0 08 May 2021
GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving Cillian Brewitt Balint Gyevnar Samuel Garcin Stefano V. Albrecht 18 30 0 10 Mar 2021
Incorporating Domain Knowledge into Deep Neural Networks T. Dash Sharad Chitlangia Aditya Ahuja A. Srinivasan AI4CE 24 9 0 27 Feb 2021
Fast, Structured Clinical Documentation via Contextual Autocomplete D. Gopinath Monica Agrawal Luke S. Murray Steven Horng David R Karger David Sontag 26 13 0 29 Jul 2020
Knowledge Distillation: A Survey Jianping Gou B. Yu Stephen J. Maybank Dacheng Tao VLM 28 2,857 0 09 Jun 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 49 371 0 30 Apr 2020
xCos: An Explainable Cosine Metric for Face Verification Task Yu-sheng Lin Zhe-Yu Liu Yu-An Chen Yu-Siang Wang Ya-Liang Chang Winston H. Hsu CVBM 33 46 0 11 Mar 2020
Slices of Attention in Asynchronous Video Job Interviews Léo Hemamou G. Felhi Jean-Claude Martin Chloé Clavel 15 20 0 19 Sep 2019
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017