  Generalized zero-shot learning (GZSL) aims to classify samples under the
assumption that some classes are not observable during training. To bridge the
gap between the seen and unseen classes, most GZSL methods attempt to associate
the visual features of seen classes with attributes or to generate unseen
samples directly. Nevertheless, the visual features used in the prior
approaches do not necessarily encode semantically related information that the
shared attributes refer to, which degrades the model generalization to unseen
classes. To address this issue, in this paper, we propose a novel semantics
disentangling framework for the generalized zero-shot learning task (SDGZSL),
where the visual features of unseen classes are firstly estimated by a
conditional VAE and then factorized into semantic-consistent and
semantic-unrelated latent vectors. In particular, a total correlation penalty
is applied to guarantee the independence between the two factorized
representations, and the semantic consistency of which is measured by the
derived relation network. Extensive experiments conducted on four GZSL
benchmark datasets have evidenced that the semantic-consistent features
disentangled by the proposed SDGZSL are more generalizable in tasks of
canonical and generalized zero-shot learning. Our source code is available at
https://github.com/uqzhichen/SDGZSL.
