Batch Normalization Embeddings for Deep Domain Generalization

Understand

Domain generalization aims at training machine learning models to perform robustly across different and unseen domains.

• Several recent methods use multiple datasets to train models to extract domain-invariant features, hoping to generalize to unseen domains.
• Instead, first we explicitly train domain-dependant representations by using ad-hoc batch normalization layers to collect independent domain's statistics.
• Then, we propose to use these statistics to map domains in a shared latent space, where membership to a domain can be measured by means of a distance function.