  In practical scenarios where training data is limited, many predictive
signals in the data can be rather from some biases in data acquisition (i.e.,
less generalizable), so that one cannot prevent a model from co-adapting on
such (so-called) "shortcut" signals: this makes the model fragile in various
distribution shifts. To bypass such failure modes, we consider an adversarial
threat model under a mutual information constraint to cover a wider class of
perturbations in training. This motivates us to extend the standard information
bottleneck to additionally model the nuisance information. We propose an
autoencoder-based training to implement the objective, as well as practical
encoder designs to facilitate the proposed hybrid discriminative-generative
training concerning both convolutional- and Transformer-based architectures.
Our experimental results show that the proposed scheme improves robustness of
learned representations (remarkably without using any domain-specific
knowledge), with respect to multiple challenging reliability measures. For
example, our model could advance the state-of-the-art on a recent challenging
OBJECTS benchmark in novelty detection by $78.4\% \rightarrow 87.2\%$ in AUROC,
while simultaneously enjoying improved corruption, background and (certified)
adversarial robustness. Code is available at
https://github.com/jh-jeong/nuisance_ib.
