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Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Explainable Methods for Image-Based Deep Learning: A Review
https://orcid.org/0000-0003-1688-8772
Abstract 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.
Explainable Methods for Image-Based Deep Learning: A Review
https://orcid.org/0000-0003-1688-8772
Abstract 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.
Explainable Methods for Image-Based Deep Learning: A Review
https://orcid.org/0000-0003-1688-8772
Abstract 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.
Explainable Methods for Image-Based Deep Learning: A Review
Gupta, Lav Kumar (Autor:in) / Koundal, Deepika (Autor:in) / Mongia, Shweta (Autor:in)
2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction
| Wiley | 2023