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High-Resolution Class Activation Mapping

Abstract: 

Insufficient reasoning for their predictions has for long been a major drawback of neural networks and has proved to be a major obstacle for their adoption by several fields of application. This paper presents a framework for discriminative localization, which helps shed some light into the decision-making of Convolutional Neural Networks (CNN). Our framework generates robust, refined and high-quality Class Activation Maps, without impacting the CNN’s performance.

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Paper Details

Authors:
Thanos Tagaris, Maria Sdraka, Andreas Stafylopatis
Submitted On:
13 September 2019 - 5:46am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Thanos Tagaris
Paper Code:
WQ.PC.5
Document Year:
2019
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Document Files

ICIP2019_Poster.pdf

(22)

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[1] Thanos Tagaris, Maria Sdraka, Andreas Stafylopatis, "High-Resolution Class Activation Mapping", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4611. Accessed: Dec. 12, 2019.
@article{4611-19,
url = {http://sigport.org/4611},
author = {Thanos Tagaris; Maria Sdraka; Andreas Stafylopatis },
publisher = {IEEE SigPort},
title = {High-Resolution Class Activation Mapping},
year = {2019} }
TY - EJOUR
T1 - High-Resolution Class Activation Mapping
AU - Thanos Tagaris; Maria Sdraka; Andreas Stafylopatis
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4611
ER -
Thanos Tagaris, Maria Sdraka, Andreas Stafylopatis. (2019). High-Resolution Class Activation Mapping. IEEE SigPort. http://sigport.org/4611
Thanos Tagaris, Maria Sdraka, Andreas Stafylopatis, 2019. High-Resolution Class Activation Mapping. Available at: http://sigport.org/4611.
Thanos Tagaris, Maria Sdraka, Andreas Stafylopatis. (2019). "High-Resolution Class Activation Mapping." Web.
1. Thanos Tagaris, Maria Sdraka, Andreas Stafylopatis. High-Resolution Class Activation Mapping [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4611