  Generative based strategy has shown great potential in the Generalized
Zero-Shot Learning task. However, it suffers severe generalization problem due
to lacking of feature diversity for unseen classes to train a good classifier.
In this paper, we propose to enhance the generalizability of GZSL models via
improving feature diversity of unseen classes. For this purpose, we present a
novel Diverse Feature Synthesis (DFS) model. Different from prior works that
solely utilize semantic knowledge in the generation process, DFS leverages
visual knowledge with semantic one in a unified way, thus deriving
class-specific diverse feature samples and leading to robust classifier for
recognizing both seen and unseen classes in the testing phase. To simplify the
learning, DFS represents visual and semantic knowledge in the aligned space,
making it able to produce good feature samples with a low-complexity
implementation. Accordingly, DFS is composed of two consecutive generators: an
aligned feature generator, transferring semantic and visual representations
into aligned features; a synthesized feature generator, producing diverse
feature samples of unseen classes in the aligned space. We conduct
comprehensive experiments to verify the efficacy of DFS. Results demonstrate
its effectiveness to generate diverse features for unseen classes, leading to
superior performance on multiple benchmarks. Code will be released upon
acceptance.
