  In this paper, we propose to use a Conditional Generative Adversarial Network
(CGAN) for distilling (i.e. transferring) knowledge from sensor data and
enhancing low-resolution target detection. In unconstrained surveillance
settings, sensor measurements are often noisy, degraded, corrupted, and even
missing/absent, thereby presenting a significant problem for multi-modal
fusion. We therefore specifically tackle the problem of a missing modality in
our attempt to propose an algorithm based on CGANs to generate representative
information from the missing modalities when given some other available
modalities. Despite modality gaps, we show that one can distill knowledge from
one set of modalities to another. Moreover, we demonstrate that it achieves
better performance than traditional approaches and recent teacher-student
models.
