  Visual recognition research often assumes a sufficient resolution of the
region of interest (ROI). That is usually violated in practice, inspiring us to
explore the Very Low Resolution Recognition (VLRR) problem. Typically, the ROI
in a VLRR problem can be smaller than $16 \times 16$ pixels, and is challenging
to be recognized even by human experts. We attempt to solve the VLRR problem
using deep learning methods. Taking advantage of techniques primarily in super
resolution, domain adaptation and robust regression, we formulate a dedicated
deep learning method and demonstrate how these techniques are incorporated step
by step. Any extra complexity, when introduced, is fully justified by both
analysis and simulation results. The resulting \textit{Robust Partially Coupled
Networks} achieves feature enhancement and recognition simultaneously. It
allows for both the flexibility to combat the LR-HR domain mismatch, and the
robustness to outliers. Finally, the effectiveness of the proposed models is
evaluated on three different VLRR tasks, including face identification, digit
recognition and font recognition, all of which obtain very impressive
performances.
