  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.
