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While deep neural networks demonstrate state-of-the-art performance on avariety of learning tasks, their performance relies on the assumption thattrain and test distributions are the same, which may not hold in real-worldapplications. Domain generalization addresses this issue by employing multiplesource domains to build robust models that can generalize to unseen targetdomains subject to shifts in data distribution. In this paper, we proposeDomain-Free Domain Generalization (DFDG), a model-agnostic method to achievebetter generalization performance on the unseen test domain without the needfor source domain labels. DFDG uses novel strategies to learn domain-invariantclass-discriminative features. It aligns class relationships of samples throughclass-conditional soft labels, and uses saliency maps, traditionally developedfor post-hoc analysis of image classification networks, to remove superficialobservations from training inputs. DFDG obtains competitive performance on bothtime series sensor and image classification public datasets.

Robust Domain-Free Domain Generalization with Class-aware Alignment