This is an explanainability library to unveil information on neural networks.
It proposes a large variety of attribution methods, they support diverse data types (Tabular, Time Series, and Images) and diverse tasks (classification, regression, object detection, semantic segmentation).
You should have a look to the tutorials or the documentation to know more.
Xplique handles both TF and Torch frameworks for a variety of attribution methods (some of them developed by our team), concept methods (including the excellent CRAFT by Thomas Fel et al.) and Feature Visualization.
Please take a look and tell us what you think about it, any feedback welcome!
Comments
This is an explanainability library to unveil information on neural networks.
It proposes a large variety of attribution methods, they support diverse data types (Tabular, Time Series, and Images) and diverse tasks (classification, regression, object detection, semantic segmentation).
You should have a look to the tutorials or the documentation to know more.
Hi!
One of the main contributors here!
Xplique handles both TF and Torch frameworks for a variety of attribution methods (some of them developed by our team), concept methods (including the excellent CRAFT by Thomas Fel et al.) and Feature Visualization.
Please take a look and tell us what you think about it, any feedback welcome!
The library is clean and well documented