Skip to content

Latest commit

 

History

History
29 lines (27 loc) · 2.32 KB

README.md

File metadata and controls

29 lines (27 loc) · 2.32 KB
Raw

Neuroscope:An Explainable AI Toolbox for Semantic Segmentation and Image Classification of Convolutional Neural Nets

Trust in artificial intelligence (AI) predictions is a crucial point for a widespread acceptance of new technologies, especially in sensitive areas like autonomous driving. The need for tools explaining AI for deep learning of images is thus eminent. Our proposed toolbox Neuroscope addresses this demand by offering state-of-the-art visualization algorithms for image classification and newly adapted methods for semantic segmentation of convolutional neural nets (CNNs). With its easy to use graphical user interface (GUI), it provides visualization on all layers of a CNN. Due to its open model-view-controller architecture, networks generated and trained with Keras and PyTorch are processable, with an interface allowing extension to additional frameworks. We demonstrate the explanation abilities provided by Neuroscope using the example of traffic scene analysis.

Cite

If you use this code for your research, please cite as:

@Article{app11052199,
AUTHOR = {Schorr, Christian and Goodarzi, Payman and Chen, Fei and Dahmen, Tim},
TITLE = {Neuroscope: An Explainable AI Toolbox for Semantic Segmentation and Image Classification of Convolutional Neural Nets},
JOURNAL = {Applied Sciences},
VOLUME = {11},
YEAR = {2021},
NUMBER = {5},
ARTICLE-NUMBER = {2199},
URL = {https://www.mdpi.com/2076-3417/11/5/2199},
ISSN = {2076-3417},
ABSTRACT = {Trust in artificial intelligence (AI) predictions is a crucial point for a widespread acceptance of new technologies, especially in sensitive areas like autonomous driving. The need for tools explaining AI for deep learning of images is thus eminent. Our proposed toolbox Neuroscope addresses this demand by offering state-of-the-art visualization algorithms for image classification and newly adapted methods for semantic segmentation of convolutional neural nets (CNNs). With its easy to use graphical user interface (GUI), it provides visualization on all layers of a CNN. Due to its open model-view-controller architecture, networks generated and trained with Keras and PyTorch are processable, with an interface allowing extension to additional frameworks. We demonstrate the explanation abilities provided by Neuroscope using the example of traffic scene analysis.},
DOI = {10.3390/app11052199}
}