2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) 2020
DOI: 10.1109/aipr50011.2020.9425299
|View full text |Cite
|
Sign up to set email alerts
|

An Interactive Graphical Visualization Approach to CNNs and RNNs

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
1
1

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(2 citation statements)
references
References 9 publications
0
2
0
Order By: Relevance
“…A web-based interactive approach to visualize the CNN allows to view the data flow and model architecture, the weights, layer processing and interpretable aspects of the whole model [126]. The interface provides visualization techniques for CNN, and RNN [126]. For CNN model, the weights for convolutional, pooling and fully-connected layers could be viewed [126].…”
Section: Libraries Tools and Frameworkmentioning
confidence: 99%
See 1 more Smart Citation
“…A web-based interactive approach to visualize the CNN allows to view the data flow and model architecture, the weights, layer processing and interpretable aspects of the whole model [126]. The interface provides visualization techniques for CNN, and RNN [126]. For CNN model, the weights for convolutional, pooling and fully-connected layers could be viewed [126].…”
Section: Libraries Tools and Frameworkmentioning
confidence: 99%
“…The interface provides visualization techniques for CNN, and RNN [126]. For CNN model, the weights for convolutional, pooling and fully-connected layers could be viewed [126]. The interface was evaluated using surveys for both experts of DNN and non-experts and showed the approach to be effective [126].…”
Section: Libraries Tools and Frameworkmentioning
confidence: 99%