DIANNA

Deep Insight And Neural Network Analysis, DIANNA is the only Explainable AI, XAI library for scientists supporting Open Neural Network Exchange, ONNX - the de facto standard models format.

15
mentions
12
contributors

Cite this software

What DIANNA can do for you

  • Provides an easy-to-use interface for non (X)AI experts
  • Implements well-known XAI methods (LIME, RISE and Kernal SHAP) chosen by systematic and objective evaluation criteria
  • Supports the de-facto standard of neural network models - ONNX
  • Supports images, text and time-series data modalities, embedings and tabular data are currently being added
  • Comes with simple intuitive image, text and time-series benchmarks
  • Easily extendable to other XAI methods

Modern scientific challenges are often tackled with (Deep) Neural Networks (DNN). Despite their high predictive accuracy, DNNs lack inherent explainability. Many scientists do not harvest DNNs power because of lack of trust and understanding of their working. Meanwhile, the eXplainable AI (XAI) research offers some post-hoc (after training) interpretability methods that provide insight into the DNN reasoning by quantifying the relevance of individual features (image pixels, words in text, etc.) concerning the prediction. These relevance heatmaps indicate how the network has reached its decision directly in the input modality (images, text, speech etc.) of the scientific data. Representing visually the captured knowledge by the AI system can become a source of scientific insights. There are many Open Source Software (OSS) implementations of these methods, alas, supporting a single DNN format, while standards like Open Neural Network eXchange (ONNX) exist. The libraries are known mostly by the AI experts. For the adoption by the wide scientific community, understanding of the XAI methods, and well-documented and standardized OSS are needed. The DIANNA library supports the best XAI methods in the context of scientific usage providing their OSS implementation based on the ONNX standard and demonstrations on benchmark datasets. DIANNA supports images, text, time-series, and tabular data, while embeddings support is currently being worked on.

Logo of DIANNA
Keywords
Programming languages
  • Jupyter Notebook 93%
  • Python 6%
  • TeX 1%
License
  • Apache-2.0
</>Source code

Participating organisations

Natural Sciences & Engineering
Natural Sciences & Engineering
Netherlands eScience Center
SURF

Reference papers

Mentions

How to find your Artificial Intelligence explainer

Author(s): Elena Ranguelova
Published in 2022

Testimonials

DIANNA is easy to implement into my codes. It provides various methods for heat maps. DIANNA is pretty flexible as it does not require specific kinds of models or data.
Climate researcher, Lorennz workshop 2022
What I like about DIANNA is the logo ;-), that it has a very easy interface, it is model agnostic and supports various methods and auto-tuning of parameters.
Sem Vijverberg, Vrije Universiteit Amsterdam
Fast, easy to use, comprehensive, nice visualizations.
Philine Bommer, XAI researcher, Technische Universität Berlin

Contributors

Elena Ranguelova
Elena Ranguelova
Project lead
Netherlands eScience Center
Christiaan Meijer
Christiaan Meijer
Scrum master, developer
Netherlands eScience Center
Yang Liu
Yang Liu
Developer
Netherlands eScience Center
Pranav Chandramouli
Pranav Chandramouli
Developer
Netherlands eScience Center
Leon Oostrum
Leon Oostrum
Developer
Netherlands eScience Center
Fakhereh Alidoost
Fakhereh Alidoost
Laura Ootes
Laura Ootes
Developer
Netherlands eScience Center
GC
Giulia Crocioni
Developer
Netherlands eScience Center
Patrick Bos
Developer, advisor
Netherlands eScience Center
Rena Bakhshi
Rena Bakhshi
Programme manager
Netherlands eScience Center

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