One of the most important characteristics of human visual intelligence is the
ability to identify unknown objects. The capability to distinguish between a
substance which a human mind has no previous experience of and a familiar
object, is innate to every human. In everyday life, within seconds of seeing an
"unknown" object, we are able to categorize it as such without any substantial
effort. Convolutional Neural Networks, regardless of how they are trained (i.e.
in a conventional manner or through transfer learning) can recognize only the
classes that they are trained for. When using them for classification, any
candidate image will be placed in one of the available classes. We propose a
low-shot classifier which can serve as the top layer to any existing CNN that
the feature extractor was already trained. Using a limited amount of labeled
data for the type of images which need to be specifically classified along with
unlabeled data for all other images, a unique target matrix and a Receiver
Operator Curve (ROC) criterion, we are able to increase identification accuracy
by up to 30% for the images that do not belong to any specific classes, while
retaining the ability to identify images that belong to the specific classes of
interest
