  Deep learning has recently been shown to be instrumental in the problem of
domain adaptation, where the goal is to learn a model on a target domain using
a similar --but not identical-- source domain. The rationale for coupling both
techniques is the possibility of extracting common concepts across domains.
Considering (strictly) local representations, traditional deep learning assumes
common concepts must be captured in the same hidden units. We contend that
jointly training a model with source and target data using a single deep
network is prone to failure when there is inherently lower-level
representational discrepancy between the two domains; such discrepancy leads to
a misalignment of corresponding concepts in separate hidden units. We introduce
a search framework to correctly align high-level representations when training
deep networks; such framework leads to the notion of conceptual --as opposed to
representational-- domain adaptation.
