Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of
Convolutional Neural Networks

Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks

15 February 2021

Konstantinos N. Plataniotis

Yeonjeong Jeong

Papers citing "Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks"

5 / 5 papers shown

Title
On The Coherence of Quantitative Evaluation of Visual Explanations Benjamin Vandersmissen José Oramas XAI FAtt 36 3 0 14 Feb 2023
TAME: Attention Mechanism Based Feature Fusion for Generating Explanation Maps of Convolutional Neural Networks Mariano V. Ntrougkas Nikolaos Gkalelis Vasileios Mezaris FAtt 19 8 0 18 Jan 2023
Learning Visual Explanations for DCNN-Based Image Classifiers Using an Attention Mechanism Ioanna Gkartzonika Nikolaos Gkalelis Vasileios Mezaris 36 9 0 22 Sep 2022
ViGAT: Bottom-up event recognition and explanation in video using factorized graph attention network Nikolaos Gkalelis Dimitrios Daskalakis Vasileios Mezaris 19 10 0 20 Jul 2022
Rethinking gradient weights' influence over saliency map estimation Masud An Nur Islam Fahim Nazmus Saqib Shafkat Khan Siam H. Jung FAtt 21 1 0 12 Jul 2022