  The paper proposes a novel technique for representing templates and instances
of concept classes. A template representation refers to the generic
representation that captures the characteristics of an entire class. The
proposed technique uses end-to-end deep learning to learn structured and
composable representations from input images and discrete labels. The obtained
representations are based on distance estimates between the distributions given
by the class label and those given by contextual information, which are modeled
as environments. We prove that the representations have a clear structure
allowing to decompose the representation into factors that represent classes
and environments. We evaluate our novel technique on classification and
retrieval tasks involving different modalities (visual and language data).
