Conditional generative adversarial networks (cGANs) have shown superior results in class-conditional generation tasks. To simultaneously control multiple conditions, cGANs require multi-label training datasets, where multiple labels can be assigned to each data instance. Nevertheless, the tremendous annotation cost limits the accessibility of multi-label datasets in real-world scenarios. Therefore, in this study we explore the practical setting called the single positive setting, where each data instance is annotated by only one positive label with no explicit negative labels. To generate multi-label data in the single positive setting, we propose a novel sampling approach called single-to-multi-label (S2M) sampling, based on the Markov chain Monte Carlo method. As a widely applicable "add-on" method, our proposed S2M sampling method enables existing unconditional and conditional GANs to draw high-quality multi-label data with a minimal annotation cost. Extensive experiments on real image datasets verify the effectiveness and correctness of our method, even when compared to a model trained with fully annotated datasets.

翻译：条件生成对抗网络（cGANs）在类别条件生成任务中已展现出卓越性能。为同时控制多个条件，cGANs需要多标签训练数据集，其中每个数据实例可被分配多个标签。然而，巨大的标注成本限制了多标签数据集在实际场景中的可获取性。因此，本研究探索了一种称为单正标签设定的实用场景，即每个数据实例仅由一个正标签进行标注，且无显式负标签。为在单正标签设定下生成多标签数据，我们提出了一种基于马尔可夫链蒙特卡洛方法的新型采样方法——单标签到多标签采样（S2M采样）。作为一种广泛适用的"附加"方法，本文提出的S2M采样方法使现有无条件GANs和条件GANs能够以最低标注成本生成高质量的多标签数据。在真实图像数据集上的大量实验验证了我们方法的有效性和正确性，即使与使用完全标注数据集训练的模型相比，其性能仍具有竞争力。