## 1 Introduction  
Over the last few years, the capabilities of artificial intelligence (AI) have dramatically increased, while modern techniques like deep neural nets have grown ever more complex and become as inscrutable as a black box. This raises the question of how trustworthy AI predictions are--which is essential in gaining widespread acceptance in society and industry. As a result, tools that answer this question and thus allow for explainable AI (XAI) are in very high demand.  
In this paper, we focus on two applications of AI: image classification and semantic segmentation. The classification of images predicts either a single or multiple categories to which the image content belongs to, but without spatial information. Semantic segmentation on the other hand, divides the image into different regions belonging to different categories. With classification one can predict that an image contains a specific object like for example a car, while semantic segmentation also provides the actual pixels where the car is predicted to be in the image [1].  
Over the past decade, convolutional neural networks (CNNs) became the state of the art for image understanding tasks like image classification, object recognition, or semantic segmentation. However, end-to-end learning strategies, i.e., only considering the inputs and outputs, make CNNs black boxes due to their complex network architecture and non-linear nature. In many cases, understanding why the model predicts a given outcome is a crucial detail for model users and a necessary diagnostic to ensure the model makes decisions based on the correct information contained in the input. With growing usage of neural networks in various areas, consequently, there is a need to explain and understand their decisions. In the case of medical diagnosis or autonomous driving in particular, it is crucial to elucidate how a particular decision was arrived at. When an incomprehensible prediction is proposed which the user cannot understand, trust in the model declines.  
To achieve state-of-the art performance, deep network architectures [2; 3] are widely used, however these highly non-linear systems are difficult to understand. While the weights of the first layer of a convolutional neural network can readily be understood as convolution kernels, the weights of consecutive layers are difficult to interpret. To facilitate interpretation, various methods have been developed, including feature visualization [4; 5; 6] and attribution [7]. Common to these techniques is that rather than interpreting the weights directly, an input image is propagated through the network, and the resulting activations are projected back to the image plane.  
While all of these methods were originally intended for classification networks, this paper proposes adaptions to semantic segmentation as well. They are included in our toolbox Neuroscience, thereby offering state-of-the-art visualization algorithms for both image classification and semantic segmentation with CNNs. We demonstrate that by utilizing visualization methods, understanding of the network architecture and its internal functionality can be improved, thus increasing user trust in the results.