# Application of the Neural Network Dependability Kit in Real-World Environments

Source: [Application of the Neural Network Dependability Kit in Real-World Environments](<https://www.sota2.com/research/paper/68fa68ef1e209131cea1f156>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:48:52.480Z

Authors: Amit Sahu, Noelia V\\'allez, Rosana Rodr\\'iguez-Bobada, Mohamad Alhaddad, Omar Moured, Georg Neugschwandtner

Published: 2020-12-14T06:53:13.000Z

## Abstract

In this paper, we provide a guideline for using the Neural Network Dependability Kit (NNDK) during the development process of NN models, and show how the algorithm is applied in two image classification use cases. The case studies demonstrate the usage of the dependability kit to obtain insights about the NN model and how they informed the development process of the neural network model. After interpreting neural networks via the different metrics available in the NNDK, the developers were able to increase the NNs' accuracy, trust the developed networks, and make them more robust. In addition, we obtained a novel application-oriented technique to provide supporting evidence for an NN's classification result to the user. In the medical image classification use case, it was used to retrieve case images from the training dataset that were similar to the current patient's image and could therefore act as a support for the NN model's decision and aid doctors in interpreting the results.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2012.09602>)

[PDF](<https://arxiv.org/pdf/2012.09602.pdf>)