Publication: Towards better understanding of gradient-based attribution methods for Deep Neural Networks
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
Date
Date
Date
Citations
Ancona, M., Ceolini, E., Öztireli, C., & Gross, M. (2018). Towards better understanding of gradient-based attribution methods for Deep Neural Networks (1711.06104; ArXiv.Org). https://doi.org/10.48550/arXiv.1711.06104
Abstract
Abstract
Abstract
Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years. While several methods have been proposed to explain network predictions, there have been only a few attempts to compare them from a theoretical perspective. What is more, no exhaustive empirical comparison has been performed in the past. In this work, we analyze four gradient-based attribution methods and formally prove conditions of equivalence and approximation between them. By ref
Metrics
Downloads
Views
Additional indexing
Creators (Authors)
Series Name
Series Name
Series Name
Item Type
Item Type
Item Type
In collections
Language
Language
Language
Publication date
Publication date
Publication date
Date available
Date available
Date available
ISSN or e-ISSN
ISSN or e-ISSN
ISSN or e-ISSN
Additional Information
Additional Information
Additional Information
OA Status
OA Status
OA Status
Free Access at
Free Access at
Free Access at
Publisher DOI
Metrics
Downloads
Views
Citations
Ancona, M., Ceolini, E., Öztireli, C., & Gross, M. (2018). Towards better understanding of gradient-based attribution methods for Deep Neural Networks (1711.06104; ArXiv.Org). https://doi.org/10.48550/arXiv.1711.06104
