Conditional generative adversarial networks (cGANs) have shown superior results in class-conditional generation tasks. In order 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 the real-world scenarios. Hence, we explore the practical setting called 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 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：有条件的基因对抗网络(cGANs)在等级条件生成任务中显示出优异的结果。 为了同时控制多重条件, cGANs需要多标签培训数据集, 给每个数据实例指定多个标签。 尽管如此, 巨大的注解成本限制了现实世界情景中多标签数据集的可访问性。 因此, 我们探索了称为单一肯定设置的实际设置, 每个数据实例仅用一个没有明显负标签的正面标签附加说明。 为了在单一正设置中生成多标签数据, 我们提议采用新颖的取样方法, 称为单到多标签(S2M)取样, 以Markov 链 Monte Carlo方法为基础。 作为广泛适用的“ 附加” 方法, 我们提议的S2M取样使现有的无条件和有条件的多标签数据集能够以最低的注解成本提取高质量的高质量多标签数据。 对真实图像数据集进行广泛的实验可以验证我们的方法的有效性和正确性, 即使与经过充分注解的数据集培训的模型相比。