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We strive to learn a model from a set of source domains that generalizes wellto unseen target domains. The main challenge in such a domain generalizationscenario is the unavailability of any target domain data during training,resulting in the learned model not being explicitly adapted to the unseentarget domains. We propose learning to generalize across domains on single testsamples. We leverage a meta-learning paradigm to learn our model to acquire theability of adaptation with single samples at training time so as to furtheradapt itself to each single test sample at test time. We formulate theadaptation to the single test sample as a variational Bayesian inferenceproblem, which incorporates the test sample as a conditional into thegeneration of model parameters. The adaptation to each test sample requiresonly one feed-forward computation at test time without any fine-tuning orself-supervised training on additional data from the unseen domains. Extensiveablation studies demonstrate that our model learns the ability to adapt modelsto each single sample by mimicking domain shifts during training. Further, ourmodel achieves at least comparable -- and often better -- performance thanstate-of-the-art methods on multiple benchmarks for domain generalization.

Learning to Generalize across Domains on Single Test Samples