Embedding-aware generative model (EAGM) addresses the data insufficiency problem for zero-shot learning (ZSL) by constructing a generator between semantic and visual embedding 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 purpose of this paper is three-fold. First, given the fact that the current embedding features in benchmark datasets are somehow out-of-date, we improve the performance of EAGMs for ZSL remarkably with embarrassedly simple modifications on the embedding features. This is an important contribution, since the results reveal that the embedding of EAGMs deserves more attention. Second, we compare and analyze a significant number of EAGMs in depth. Based on five benchmark datasets, we update the state-of-the-art results for ZSL and give a strong baseline for few-shot learning (FSL), including the classic unseen-class few-shot learning (UFSL) and the more challenging seen-class few-shot learning (SFSL). Finally, a comprehensive generative model repository, namely, generative any-shot learning (GASL) repository, is provided, which contains the models, features, parameters, and settings of EAGMs for ZSL and FSL. Any results in this paper can be readily reproduced with only one command line based on GASL.

翻译：嵌入感知生成模型（EAGM）通过构建语义与视觉嵌入空间之间的生成器，解决了零样本学习（ZSL）中的数据不足问题。得益于预定义的基准测试与协议，针对ZSL提出的EAGM数量正快速增加。我们认为，是时候退一步重新审视嵌入感知生成范式了。本文的目的有三方面。首先，鉴于当前基准数据集中的嵌入特征已略显过时，我们通过对嵌入特征进行极其简单的修改，显著提升了EAGM在ZSL中的性能。这是一项重要贡献，因为结果揭示了EAGM的嵌入值得更多关注。其次，我们深入比较并分析了大量EAGM。基于五个基准数据集，我们更新了ZSL的最优结果，并为少样本学习（FSL）提供了强有力的基线，包括经典的未见类少样本学习（UFSL）和更具挑战性的已见类少样本学习（SFSL）。最后，我们提供了一个全面的生成模型库，即生成式任意样本学习（GASL）库，其中包含用于ZSL和FSL的EAGM的模型、特征、参数及设置。基于GASL，本文中的任何结果均可通过单条命令行轻松复现。