  State-of-the-art stereo matching networks trained only on synthetic data
often fail to generalize to more challenging real data domains. In this paper,
we attempt to unfold an important factor that hinders the networks from
generalizing across domains: through the lens of shortcut learning. We
demonstrate that the learning of feature representations in stereo matching
networks is heavily influenced by synthetic data artefacts (shortcut
attributes). To mitigate this issue, we propose an Information-Theoretic
Shortcut Avoidance~(ITSA) approach to automatically restrict shortcut-related
information from being encoded into the feature representations. As a result,
our proposed method learns robust and shortcut-invariant features by minimizing
the sensitivity of latent features to input variations. To avoid the
prohibitive computational cost of direct input sensitivity optimization, we
propose an effective yet feasible algorithm to achieve robustness. We show that
using this method, state-of-the-art stereo matching networks that are trained
purely on synthetic data can effectively generalize to challenging and
previously unseen real data scenarios. Importantly, the proposed method
enhances the robustness of the synthetic trained networks to the point that
they outperform their fine-tuned counterparts (on real data) for challenging
out-of-domain stereo datasets.
