Embedding-aware generative model (EAGM) addresses the data insufficiency
problem for zero-shot learning (ZSL) by constructing a generator between
semantic and visual feature spaces. Thanks to the predefined benchmark and
protocols, the number of proposed EAGMs for ZSL is increasing rapidly. We argue
that it is time to take a step back and reconsider the embedding-aware
generative paradigm. The main work of this paper is two-fold. First, the
embedding features in benchmark datasets are somehow overlooked, which
potentially limits the performance of EAGMs, while most researchers focus on
how to improve EAGMs. Therefore, we conduct a systematic evaluation of ten
representative EAGMs and prove that even embarrassedly simple modifications on
the embedding features can improve the performance of EAGMs for ZSL remarkably.
So it's time to pay more attention to the current embedding features in
benchmark datasets. Second, based on five benchmark datasets, each with six
any-shot learning scenarios, we systematically compare the performance of ten
typical EAGMs for the first time, and we give a strong baseline for zero-shot
learning (ZSL) and few-shot learning (FSL). Meanwhile, a comprehensive
generative model repository, namely, generative any-shot learning (GASL)
repository, is provided, which contains the models, features, parameters, and
scenarios of EAGMs for ZSL and FSL. Any results in this paper can be readily
reproduced with only one command line based on GASL