  While deep learning has led to huge progress in complex image classification
tasks like ImageNet, unexpected failure modes, e.g. via spurious features, call
into question how reliably these classifiers work in the wild. Furthermore, for
safety-critical tasks the black-box nature of their decisions is problematic,
and explanations or at least methods which make decisions plausible are needed
urgently. In this paper, we address these problems by generating images that
optimize a classifier-derived objective using a framework for guided image
generation. We analyze the decisions of image classifiers by visual
counterfactual explanations (VCEs), detection of systematic mistakes by
analyzing images where classifiers maximally disagree, and visualization of
neurons and spurious features. In this way, we validate existing observations,
e.g. the shape bias of adversarially robust models, as well as novel failure
modes, e.g. systematic errors of zero-shot CLIP classifiers. Moreover, our VCEs
outperform previous work while being more versatile.
