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An Interpretability Optimization Method for Deep Learning Networks Based on Grad-CAM

IEEE Internet of Things Journal
In the modern era, the optimization of energy utilization in both industrial production and everyday life represents a critical challenge. This challenge underscores the imperative for innovative technological solutions, with artificial intelligence (AI)
Yubo Zhang +4 more
semanticscholar +1 more source

Multilayer Grad-CAM: An effective tool towards explainable deep neural networks for intelligent fault diagnosis

Journal of Manufacturing Systems, 2023
Ruqiang Yan, Xue-Feng Chen
exaly

Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging

Journal of Neuroscience Methods, 2021
Yunyan Zhang
exaly

GRAd-COV2, a gorilla adenovirus-based candidate vaccine against COVID-19, is safe and immunogenic in younger and older adults

Science Translational Medicine, 2022
Giuseppe Ippolito, Stefania Capone, Emanuele Nicastri +2 more
exaly

Arrhythmia detection model using modified DenseNet for comprehensible Grad-CAM visualization

Biomedical Signal Processing and Control, 2022
Sung Won Han
exaly

Grad-seq shines light on unrecognized RNA and protein complexes in the model bacterium Escherichia coli

Nucleic Acids Research, 2020
Jens Hör, Silvia Di Giorgio, Milan Gerovac +2 more
exaly

Analysis of a photosynthetic cyanobacterium rich in internal membrane systems via gradient profiling by sequencing (Grad-seq)

Plant Cell, 2021
Matthias Riediger, Philipp Spaet, Raphael Bilger +2 more
exaly