A fundamental shortcoming of deep neural networks is their specialization to a single task and domain. While multi-domain learning enables the learning of compact models that span multiple visual domains, these rely on the presence of domain labels, in turn requiring laborious curation of datasets. This paper proposes a less explored, but highly realistic new setting called latent domain learning: learning over data from different domains, without access to domain annotations. Experiments show that this setting is challenging for standard models and existing multi-domain approaches, calling for new customized solutions: a sparse adaptation strategy is formulated which enhances performance by accounting for latent domains in data. Our method can be paired seamlessly with existing models, and benefits conceptually related tasks, e.g. empirical fairness problems and long-tailed recognition.