  Making a single network effectively address diverse contexts---learning the
variations within a dataset or multiple datasets---is an intriguing step
towards achieving generalized intelligence. Existing approaches of deepening,
widening, and assembling networks are not cost effective in general. In view of
this, networks which can allocate resources according to the context of the
input and regulate flow of information across the network are effective. In
this paper, we present Context-Aware Multipath Network (CAMNet), a multi-path
neural network with data-dependant routing between parallel tensors. We show
that our model performs as a generalized model capturing variations in
individual datasets and multiple different datasets, both simultaneously and
sequentially. CAMNet surpasses the performance of classification and
pixel-labeling tasks in comparison with the equivalent single-path, multi-path,
and deeper single-path networks, considering datasets individually,
sequentially, and in combination. The data-dependent routing between tensors in
CAMNet enables the model to control the flow of information end-to-end,
deciding which resources to be common or domain-specific.
