Under CopyrightHeide, Nina FelicitasNina FelicitasHeideAlbrecht, AlexanderAlexanderAlbrechtHeizmann, MichaelMichaelHeizmann2022-03-1421.11.20202020https://publica.fraunhofer.de/handle/publica/40922610.24406/publica-fhg-409226We propose a methodology for generalized exploratory data analysis focusing on artificial neural network (ANN) methods. Our method is denoted IC-ACC due to the combined assessment of information content (IC) and accuracy (ACC) and aims at answering a frequently posed question in ANN research: ""What is good data?"" As the dataset has the primary influence on the development of the model, IC-ACC provides a step towards explainable ANN methods in the pre-modeling stage by a better insight in the dataset. With this insight, detrimental data can be eliminated before a negative influence on the ANN performance occurs. IC-ACC constitutes a guideline to generate efficient and accurate data for a specific, data-driven ANN method. Moreover, we show that training an ANN for the semantic segmentation of 3Ddata from unstructured environments with IC-ACC-assessed and -customized training data contributes to a more efficient training. The IC-ACC method is demonstrated on application examples for the visual perception of robotic platforms.enartificial neural networkimage processingpremodeling explainabilityrobot vision system004670conference paper