POEM: Polarization of Embeddings for Domain-Invariant Representations
Sang-Yeong Jo Sung Whan Yoon
Handling out-of-distribution samples is a long-lasting challenge for deep
visual models. In particular, domain generalization (DG) is one of the most
relevant tasks that aims to train a model with a generalization capability on
novel domains. Most existing DG approaches share the same philosophy to
minimize the discrepancy between domains by finding the domain-invariant
representations. On the contrary, our proposed method called POEM acquires a
strong DG capability by learning domain-invariant and domain-specific
representations and polarizing them. Specifically, POEM cotrains
category-classifying and domain-classifying embeddings while regularizing them
to be orthogonal via minimizing the cosine-similarity between their features,
i.e., the polarization of embeddings. The clear separation of embeddings
suppresses domain-specific features in the domain-invariant embeddings. The
concept of POEM shows a unique direction to enhance the domain robustness of
representations that brings considerable and consistent performance gains when
combined with existing DG methods. Extensive simulation results in popular DG
benchmarks with the PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet
datasets show that POEM indeed facilitates the category-classifying embedding
to be more domain-invariant.