  Out-of-distribution (OOD) detection empowers the model trained on the closed
image set to identify unknown data in the open world. Though many prior
techniques have yielded considerable improvements in this research direction,
two crucial obstacles still remain. Firstly, a unified perspective has yet to
be presented to view the developed arts with individual designs, which is vital
for providing insights into future work. Secondly, we expect sufficient natural
OOD supervision to promote the generation of compact boundaries between the
in-distribution (ID) and OOD data without collecting explicit OOD samples. To
tackle these issues, we propose a general probabilistic framework to interpret
many existing methods and an OOD-data-free model, namely
\textbf{S}elf-supervised \textbf{S}ampling for \textbf{O}OD \textbf{D}etection
(SSOD). SSOD efficiently exploits natural OOD signals from the ID data based on
the local property of convolution. With these supervisions, it jointly
optimizes the OOD detection and conventional ID classification in an end-to-end
manner. Extensive experiments reveal that SSOD establishes competitive
state-of-the-art performance on many large-scale benchmarks, outperforming the
best previous method by a large margin, \eg, reporting \textbf{-6.28\%} FPR95
and \textbf{+0.77\%} AUROC on ImageNet, \textbf{-19.01\%} FPR95 and
\textbf{+3.04\%} AUROC on CIFAR-10, and top-ranked performance on hard OOD
datasets, \ie, ImageNet-O and OpenImage-O.
