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.

翻译：嵌入感知生成模型通过构建语义空间与视觉特征空间之间的生成器，解决了零样本学习中的数据不足问题。得益于预定义的基准测试和协议，针对零样本学习的嵌入感知生成模型数量正迅速增长。我们认为，是时候退一步重新审视嵌入感知生成范式了。本文的主要工作包括两个方面。首先，基准数据集中的嵌入特征在一定程度上被忽视了，这潜在地限制了嵌入感知生成模型的性能，而大多数研究者关注的是如何改进嵌入感知生成模型。因此，我们对十种代表性嵌入感知生成模型进行了系统评估，并证明即使对嵌入特征进行极其简单的修改也能显著提升嵌入感知生成模型在零样本学习中的性能。因此，当前应更加关注基准数据集中的嵌入特征。其次，基于五个基准数据集（每个数据集包含六种任意样本学习场景），我们首次系统比较了十种典型嵌入感知生成模型的性能，并为零样本学习和少样本学习提供了强有力的基线。同时，提供了一个全面的生成模型库，即生成式任意样本学习库，其中包含面向零样本学习和少样本学习的嵌入感知生成模型的模型、特征、参数和场景。基于生成式任意样本学习库，本文的所有结果均可通过单行命令轻松复现。