  Lately, generative adversarial networks (GANs) have been successfully applied
to zero-shot learning (ZSL) and achieved state-of-the-art performance. By
synthesizing virtual unseen visual features, GAN-based methods convert the
challenging ZSL task into a supervised learning problem. However, GAN-based ZSL
methods have to train the generator on the seen categories and further apply it
to unseen instances. An inevitable issue of such a paradigm is that the
synthesized unseen features are prone to seen references and incapable to
reflect the novelty and diversity of real unseen instances. In a nutshell, the
synthesized features are confusing. One cannot tell unseen categories from seen
ones using the synthesized features. As a result, the synthesized features are
too subtle to be classified in generalized zero-shot learning (GZSL) which
involves both seen and unseen categories at the test stage. In this paper, we
first introduce the feature confusion issue. Then, we propose a new feature
generating network, named alleviating feature confusion GAN (AFC-GAN), to
challenge the issue. Specifically, we present a boundary loss which maximizes
the decision boundary of seen categories and unseen ones. Furthermore, a novel
metric named feature confusion score (FCS) is proposed to quantify the feature
confusion. Extensive experiments on five widely used datasets verify that our
method is able to outperform previous state-of-the-arts under both ZSL and GZSL
protocols.
