  With the maturing of deep learning systems, trustworthiness is becoming
increasingly important for model assessment. We understand trustworthiness as
the combination of explainability and robustness. Generative classifiers (GCs)
are a promising class of models that are said to naturally accomplish these
qualities. However, this has mostly been demonstrated on simple datasets such
as MNIST and CIFAR in the past. In this work, we firstly develop an
architecture and training scheme that allows GCs to operate on a more relevant
level of complexity for practical computer vision, namely the ImageNet
challenge. Secondly, we demonstrate the immense potential of GCs for
trustworthy image classification. Explainability and some aspects of robustness
are vastly improved compared to feed-forward models, even when the GCs are just
applied naively. While not all trustworthiness problems are solved completely,
we observe that GCs are a highly promising basis for further algorithms and
modifications. We release our trained model for download in the hope that it
serves as a starting point for other generative classification tasks, in much
the same way as pretrained ResNet architectures do for discriminative
classification.
