Improving Feature Attribution through Input-specific Network Pruning

Improving Feature Attribution through Input-specific Network Pruning

25 November 2019

Soroosh Baselizadeh

Saurabh Khanduja

Christian Rupprecht

Nassir Navab

Papers citing "Improving Feature Attribution through Input-specific Network Pruning"

7 / 7 papers shown

Title
SPES: Spectrogram Perturbation for Explainable Speech-to-Text Generation Dennis Fucci Marco Gaido Beatrice Savoldi Matteo Negri Mauro Cettolo L. Bentivogli 57 1 0 03 Nov 2024
sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI Xin Zhang Liangxiu Han Lianghao Han Haoming Chen Darren Dancey Daoqiang Zhang MedIm 18 4 0 17 Feb 2023
Less is More: The Influence of Pruning on the Explainability of CNNs David Weber F. Merkle Pascal Schöttle Stephan Schlögl Martin Nocker FAtt 34 1 0 17 Feb 2023
Explainable Model-Agnostic Similarity and Confidence in Face Verification Martin Knoche Torben Teepe S. Hörmann Gerhard Rigoll AAML CVBM 27 17 0 24 Nov 2022
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound Arushi Gupta Nikunj Saunshi Dingli Yu Kaifeng Lyu Sanjeev Arora AAML FAtt XAI 31 5 0 05 Nov 2022
Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models Ashkan Khakzar Yawei Li Yang Zhang Mirac Sanisoglu Seong Tae Kim Mina Rezaei Bernd Bischl Nassir Navab 35 0 0 04 Apr 2022
Adversarial Infidelity Learning for Model Interpretation Jian Liang Bing Bai Yuren Cao Kun Bai Fei-Yue Wang AAML 54 18 0 09 Jun 2020