HIGH-RESOLUTION CLASS ACTIVATION MAPPING

被引：0

作者：

Tagaris, Thanos ^{[1
]}

Sdraka, Maria ^{[1
]}

Stafylopatis, Andreas ^{[1
]}

机构：

[1] Natl Tech Univ Athens, Sch Elect & Comp Engn, Athens, Greece

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2019年

关键词：

Discriminative localization; Class Activation Map; Deep Learning; Convolutional Neural Networks;

D O I：

10.1109/icip.2019.8803474

中图分类号：

TB8 [摄影技术];

学科分类号：

0804 ;

摘要：

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.

引用

页码：4514 / 4518

页数：5

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