.png){kind=icon}
Return
Explainable Methods for Image-Based Deep Learning: A Review
.png){kind=icon}
PRE
AI
DOI：10.1007/s11831-023-09881-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With Artificial Intelligence advancement, Deep neural networks (DNN) are extensively used for decision-making in intelligent systems. However, improved performance and accuracy have been achieved through the increasing use of complex models, which makes it challenging for users to understand and trust. This ambiguous nature of these Deep machine learning models of high accuracy and low interpretability is problematic for their adoption in critical domains where it is vital to be able to explain the decisions made by the system. Explainable Artificial Intelligence has become an exciting field for explaining and interpreting machine learning models. Among the different data types used in machine learning, image data is considered hard to train because of the factors such as class, scale, viewpoint, and background variations. This paper aims to provide a rounded view of emerging methods to explain DNN models as a way to boost transparency in image-based deep learning with the analysis of the current and upcoming trends.
Keywords:
BLACK-BOX
Journal
IF:
12.1
Papers:
1.8K
Citations:
1.2W
Organization
No organization information available
Cited Papers
ShareSavePerformance evaluation of a novel cross-laminated timber made from flattened bamboo and wood lumber.png)
BioResources
OAAI
BioResourcesIF0
OAAIShareSaveShareSaveShareSaveShareSaveHemimicropsia in a Patient with a Cavernoma of the Visual Association Cortex: Imaging Analysis Utilizing a New 3D Brain Atlas System and fMRINeuroImage
NeuroImageIF0
PREAI
ShareSave
View more