# Out-of-Domain Robustness via Targeted Augmentations

## TL;DR

This paper introduces targeted data augmentations that selectively randomize non-robust features to improve out-of-domain generalization, demonstrating significant performance gains on real-world datasets.

## Contribution

It proposes a novel targeted augmentation method based on theoretical insights, outperforming existing generic and domain-invariant approaches for OOD robustness.

## Key findings

- Targeted augmentations improve OOD performance by 3.2-15.2 percentage points.
- Theoretical analysis supports the effectiveness of selective feature randomization.
- Experiments on three datasets validate the state-of-the-art results.

## Abstract

Models trained on one set of domains often suffer performance drops on unseen domains, e.g., when wildlife monitoring models are deployed in new camera locations. In this work, we study principles for designing data augmentations for out-of-domain (OOD) generalization. In particular, we focus on real-world scenarios in which some domain-dependent features are robust, i.e., some features that vary across domains are predictive OOD. For example, in the wildlife monitoring application above, image backgrounds vary across camera locations but indicate habitat type, which helps predict the species of photographed animals. Motivated by theoretical analysis on a linear setting, we propose targeted augmentations, which selectively randomize spurious domain-dependent features while preserving robust ones. We prove that targeted augmentations improve OOD performance, allowing models to generalize better with fewer domains. In contrast, existing approaches such as generic augmentations, which fail to randomize domain-dependent features, and domain-invariant augmentations, which randomize all domain-dependent features, both perform poorly OOD. In experiments on three real-world datasets, we show that targeted augmentations set new states-of-the-art for OOD performance by 3.2-15.2 percentage points.