  Zero-shot learning (ZSL) enables solving a task without the need to see its
examples. In this paper, we propose two ZSL frameworks that learn to synthesize
parameters for novel unseen classes. First, we propose to cast the problem of
ZSL as learning manifold embeddings from graphs composed of object classes,
leading to a flexible approach that synthesizes "classifiers" for the unseen
classes. Then, we define an auxiliary task of synthesizing "exemplars" for the
unseen classes to be used as an automatic denoising mechanism for any existing
ZSL approaches or as an effective ZSL model by itself. On five visual
recognition benchmark datasets, we demonstrate the superior performances of our
proposed frameworks in various scenarios of both conventional and generalized
ZSL. Finally, we provide valuable insights through a series of empirical
analyses, among which are a comparison of semantic representations on the full
ImageNet benchmark as well as a comparison of metrics used in generalized ZSL.
Our code and data are publicly available at
https://github.com/pujols/Zero-shot-learning-journal
