.png){kind=icon}
返回
Visualization Methods for Image Transformation Convolutional Neural Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2018.2881194.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Convolutional neural networks (CNNs) are powerful machine learning models that have become the state of the art in several problems in the areas of computer vision and image processing. Nevertheless, the knowledge of why and how these models present an impressive performance is still limited. There are visualization techniques that can help us to understand the inner working of neural networks. However, they have mostly been applied to classification models. In this paper, we evaluate the application of visualization methods to networks where the input and output are images of proportional dimensions. The results show that visualization brings visual cues associated with how these systems work, helping in their understanding and improvement. We use the knowledge obtained from the visualization of an image restoration CNN to improve the architecture's efficiency with no significant degradation of its performance.
Keyword:
Activation maximization
convolutional neural networks (CNNs)
deep visualization
dehazing
underwater
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
Determination of mechanical response of non-linear viscoelastic solids based on frechet expansion
PREAI
分享收藏分享收藏Effect of chloride-induced reinforcing steel corrosion on the flexural strength of reinforced concrete beams氯化物引起的钢筋腐蚀对钢筋混凝土梁抗弯强度的影响
PREAI
分享收藏ScanFold 2.0: a rapid approach for identifying potential structured RNA targets in genomes and transcriptomes.png)
PeerJ
OAAI
PeerJIF0
OAAI分享收藏Structural study of lanthanides(III) in aqueous nitrate and chloride solutions by EXAFS通过EXAFS对硝酸盐和氯化物水溶液中镧系元素 (III) 的结构研究
PREAI
分享收藏
查看更多内容