  Open Domain Generalization (ODG) is a challenging task as it not only deals
with distribution shifts but also category shifts between the source and target
datasets. To handle this task, the model has to learn a generalizable
representation that can be applied to unseen domains while also identify
unknown classes that were not present during training. Previous work has used
multiple source-specific networks, which involve a high computation cost.
Therefore, this paper proposes a method that can handle ODG using only a single
network. The proposed method utilizes a head that is pre-trained by
linear-probing and employs two regularization terms, each targeting the
regularization of feature extractor and the classification head, respectively.
The two regularization terms fully utilize the pre-trained features and
collaborate to modify the head of the model without excessively altering the
feature extractor. This ensures a smoother softmax output and prevents the
model from being biased towards the source domains. The proposed method shows
improved adaptability to unseen domains and increased capability to detect
unseen classes as well. Extensive experiments show that our method achieves
competitive performance in several benchmarks. We also justify our method with
careful analysis of the effect on the logits, features, and the head.
