  Synthesizing pseudo samples is currently the most effective way to solve the
Generalized Zero-Shot Learning (GZSL) problem. Most models achieve competitive
performance but still suffer from two problems: (1) Feature confounding, the
overall representations confound task-correlated and task-independent features,
and existing models disentangle them in a generative way, but they are
unreasonable to synthesize reliable pseudo samples with limited samples; (2)
Distribution uncertainty, that massive data is needed when existing models
synthesize samples from the uncertain distribution, which causes poor
performance in limited samples of seen classes. In this paper, we propose a
non-generative model to address these problems correspondingly in two modules:
(1) Task-correlated feature disentanglement, to exclude the task-correlated
features from task-independent ones by adversarial learning of domain adaption
towards reasonable synthesis; (2) Controllable pseudo sample synthesis, to
synthesize edge-pseudo and center-pseudo samples with certain characteristics
towards more diversity generated and intuitive transfer. In addation, to
describe the new scene that is the limit seen class samples in the training
process, we further formulate a new ZSL task named the 'Few-shot Seen class and
Zero-shot Unseen class learning' (FSZU). Extensive experiments on four
benchmarks verify that the proposed method is competitive in the GZSL and the
FSZU tasks.
