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Full-Spectrum Out-of-Distribution Detection
[article]
2022
arXiv
pre-print
Existing out-of-distribution (OOD) detection literature clearly defines semantic shift as a sign of OOD but does not have a consensus over covariate shift. Samples experiencing covariate shift but not semantic shift are either excluded from the test set or treated as OOD, which contradicts the primary goal in machine learning -- being able to generalize beyond the training distribution. In this paper, we take into account both shift types and introduce full-spectrum OOD (FS-OOD) detection, a
arXiv:2204.05306v1
fatcat:h2in6mczzre2rgm2ty3mh2qhvi