  In some of object recognition problems, labeled data may not be available for
all categories. Zero-shot learning utilizes auxiliary information (also called
signatures) describing each category in order to find a classifier that can
recognize samples from categories with no labeled instance. In this paper, we
propose a novel semi-supervised zero-shot learning method that works on an
embedding space corresponding to abstract deep visual features. We seek a
linear transformation on signatures to map them onto the visual features, such
that the mapped signatures of the seen classes are close to labeled samples of
the corresponding classes and unlabeled data are also close to the mapped
signatures of one of the unseen classes.
  We use the idea that the rich deep visual features provide a representation
space in which samples of each class are usually condensed in a cluster. The
effectiveness of the proposed method is demonstrated through extensive
experiments on four public benchmarks improving the state-of-the-art prediction
accuracy on three of them.
