  Deep Learning is a state-of-the-art technique to make inference on extensive
or complex data. As a black box model due to their multilayer nonlinear
structure, Deep Neural Networks are often criticized to be non-transparent and
their predictions not traceable by humans. Furthermore, the models learn from
artificial datasets, often with bias or contaminated discriminating content.
Through their increased distribution, decision-making algorithms can contribute
promoting prejudge and unfairness which is not easy to notice due to lack of
transparency. Hence, scientists developed several so-called explanators or
explainers which try to point out the connection between input and output to
represent in a simplified way the inner structure of machine learning black
boxes. In this survey we differ the mechanisms and properties of explaining
systems for Deep Neural Networks for Computer Vision tasks. We give a
comprehensive overview about taxonomy of related studies and compare several
survey papers that deal with explainability in general. We work out the
drawbacks and gaps and summarize further research ideas.
