State of the Art of Visual Analytics for eXplainable Deep Learning

no code implementations • Computer Graphics Forum 2023 • Biagio La Rosa, Graziano Blasilli, Romain Bourqui, David Auber, Giuseppe Santucci, Roberto Capobianco, Enrico Bertini, Romain Giot, Marco Angelini

The survey concludes by identifying future research challenges and bridging activities that are helpful to strengthen the role of Visual Analytics as effective support for eXplainable Deep Learning and to foster the adoption of Visual Analytics solutions in the eXplainable Deep Learning community.

Deep Learning Survey

Deep Neural Network for DrawiNg Networks, (DNN)^2

1 code implementation • 8 Aug 2021 • Loann Giovannangeli, Frederic Lalanne, David Auber, Romain Giot, Romain Bourqui

We demonstrate that it is possible to use DL techniques to learn a graph-to-layout sequence of operations thanks to a graph-related objective function.

Impacts of the Numbers of Colors and Shapes on Outlier Detection: from Automated to User Evaluation

no code implementations • 10 Mar 2021 • Loann Giovannangeli, Romain Giot, David Auber, Romain Bourqui

When encoded with one attribute, the difficulty depends on that attribute heterogeneity until its capacity limit (7 for color, 5 for shape) is reached.

Attribute Outlier Detection