  In this paper, we address zero-shot learning (ZSL), the problem of
recognizing categories for which no labeled visual data are available during
training. We focus on the transductive setting, in which unlabelled visual data
from unseen classes is available. State-of-the-art paradigms in ZSL typically
exploit generative adversarial networks to synthesize visual features from
semantic attributes. We posit that the main limitation of these approaches is
to adopt a single model to face two problems: 1) generating realistic visual
features, and 2) translating semantic attributes into visual cues. Differently,
we propose to decouple such tasks, solving them separately. In particular, we
train an unconditional generator to solely capture the complexity of the
distribution of visual data and we subsequently pair it with a conditional
generator devoted to enrich the prior knowledge of the data distribution with
the semantic content of the class embeddings. We present a detailed ablation
study to dissect the effect of our proposed decoupling approach, while
demonstrating its superiority over the related state-of-the-art.
