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Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples
[article]
2021
arXiv
pre-print
In this paper, we argue that the unsatisfactory out-of-distribution (OOD) detection performance of neural networks is mainly due to the SoftMax loss anisotropy and propensity to produce low entropy probability distributions in disagreement with the principle of maximum entropy. Current out-of-distribution (OOD) detection approaches usually do not directly fix the SoftMax loss drawbacks, but rather build techniques to circumvent it. Unfortunately, those methods usually produce undesired side
arXiv:2006.04005v3
fatcat:noxs7ektcbavlingmy3p533fxu