Due to the excellent semantics extraction capabilities, deep learning methods have significantly progressed in salient object detection (SOD). However, these methods often require time-consuming pre-training and large training datasets with ground truth. To address these issues, by referring to the framework known as “deep image prior (DIP),” we propose a SOD method called deep label prior network (DLPNet), which consists of$\mathcal A$-stream and$\mathcal B$-stream. The$\mathcal A$-stream includes two cascaded UNets and a simple CNNs module to extract the initial saliency map, while the$\mathcal B$-stream contains only two cascaded UNets, which refines the extracted initial saliency map. Unlike most of the current deep learning methods, DLPNet views the SOD task as a conditional image generation problem, relying on only the internal prior of the input itself to generate the saliency map. Hence, our DLPNet does not require pre-training or large annotated / unannotated datasets. Furthermore, we propose a morphology operation scheme, which creates rich pseudo-labels for facilitating the updating of network weights. Extensive experiments demonstrate that our method outperforms state-of-the-art unsupervised techniques and is even comparable to state-of-the-art supervised and weakly supervised methods on different evaluation metrics.