  We introduce a simple yet effective episode-based training framework for
zero-shot learning (ZSL), where the learning system requires to recognize
unseen classes given only the corresponding class semantics. During training,
the model is trained within a collection of episodes, each of which is designed
to simulate a zero-shot classification task. Through training multiple
episodes, the model progressively accumulates ensemble experiences on
predicting the mimetic unseen classes, which will generalize well on the real
unseen classes. Based on this training framework, we propose a novel generative
model that synthesizes visual prototypes conditioned on the class semantic
prototypes. The proposed model aligns the visual-semantic interactions by
formulating both the visual prototype generation and the class semantic
inference into an adversarial framework paired with a parameter-economic
Multi-modal Cross-Entropy Loss to capture the discriminative information.
Extensive experiments on four datasets under both traditional ZSL and
generalized ZSL tasks show that our model outperforms the state-of-the-art
approaches by large margins.
