Out-of-distribution (OOD) detection empowers the model trained on the closed
set to identify unknown data in the open world. Though many prior techniques
have yielded considerable improvements, 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 the
related directions. Secondly, most research focuses on the post-processing
schemes of the pre-trained features while disregarding the superiority of
end-to-end training, dramatically limiting the upper bound of OOD detection. To
tackle these issues, we propose a general probabilistic framework to interpret
many existing methods and an OOD-data-free model, namely Self-supervised
Sampling for OOD Detection (SSOD), to unfold the potential of end-to-end
learning. SSOD efficiently exploits natural OOD signals from the
in-distribution (ID) data based on the local property of convolution. With
these supervisions, it jointly optimizes the OOD detection and conventional ID
classification. Extensive experiments reveal that SSOD establishes competitive
state-of-the-art performance on many large-scale benchmarks, where it
outperforms the most recent approaches, such as KNN, by a large margin, e.g.,
48.99% to 35.52% on SUN at FPR95.

本论文提出了一种新的概率框架模型和一种无需 OOD 数据的自监督抽样模型 (SSOD)，以解决现有研究中的问题，并展示了这些模型在大规模基准测试上具有领先的性能.