Oct 29, 2019 · We adopt a model-agnostic learning paradigm with gradient-based meta-train and meta-test procedures to expose the optimization to domain shift.

We introduce two complementary losses which explicitly regularize the semantic structure of the feature space via a model-agnostic episodic learning procedure.

We study the challenging problem of domain generalization, i.e., training a model on multi-domain source data such that it can directly generalize to unseen ...

We adopt a model-agnostic learning paradigm with gradient-based meta-train and meta-test procedures to expose the optimization to domain shift. Further, we ...

Nov 18, 2020 · This paper proposes a new domain generalization method. The goal is for a model, trained on multi-domain source data, to generalize well on target domains with ...

Oct 29, 2019 · This work investigates the challenging problem of domain generalization, i.e., training a model on multi-domain source data such that it can ...

Oct 29, 2019 · We adopt a model-agnostic learning paradigm with gradient-based meta-train and meta-test procedures to expose the optimization to domain shift.

The experiments focus on learning from multiple domains then applied the model to *only one* target domain, which is, in fact, more related to the multi-source ...

Oct 29, 2019 · We adopt a model-agnostic learning paradigm with gradient-based meta-train and meta-test procedures to expose the optimization to domain shift.

Dec 13, 2022 · Bibliographic details on Domain Generalization via Model-Agnostic Learning of Semantic Features.