Generally, the present disclosure is directed to systems and methods that leverage batch normalization statistics as a way to generalize across domains. In particular, example implementations of the present disclosure can generate different representations for different domains by collecting independent batch normalization statistics, which can then be used to map between domains in a shared latent space. At test or inference time, samples from an unknown test or target domain can be projected into the same shared latent space. The domain of the target sample can therefore be expressed as a linear combination of the known ones, with the combination between weighted based on respective distances between batch normalization statistics in the latent space. This same mapping strategy can be applied at both training and test time to learn both a latent representation and a powerful but lightweight ensemble model that operates within such latent space.
展开▼
