  While deep neural networks demonstrate state-of-the-art performance on a
variety of learning tasks, their performance relies on the assumption that
train and test distributions are the same, which may not hold in real-world
applications. Domain generalization addresses this issue by employing multiple
source domains to build robust models that can generalize to unseen target
domains subject to shifts in data distribution. In this paper, we propose
Domain-Free Domain Generalization (DFDG), a model-agnostic method to achieve
better generalization performance on the unseen test domain without the need
for source domain labels. DFDG uses novel strategies to learn domain-invariant
class-discriminative features. It aligns class relationships of samples through
class-conditional soft labels, and uses saliency maps, traditionally developed
for post-hoc analysis of image classification networks, to remove superficial
observations from training inputs. DFDG obtains competitive performance on both
time series sensor and image classification public datasets.
