  Over the last decade, the development of deep image classification networks
has mostly been driven by the search for the best performance in terms of
classification accuracy on standardized benchmarks like ImageNet. More
recently, this focus has been expanded by the notion of model robustness, \ie
the generalization abilities of models towards previously unseen changes in the
data distribution. While new benchmarks, like ImageNet-C, have been introduced
to measure robustness properties, we argue that fixed testsets are only able to
capture a small portion of possible data variations and are thus limited and
prone to generate new overfitted solutions. To overcome these drawbacks, we
suggest to estimate the robustness of a model directly from the structure of
its learned feature-space. We introduce robustness indicators which are
obtained via unsupervised clustering of latent representations from a trained
classifier and show very high correlations to the model performance on
corrupted test data.
