Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces

2 code implementations • CVPR 2021 • Alireza Zaeemzadeh, Niccolo Bisagno, Zeno Sambugaro, Nicola Conci, Nazanin Rahnavard, Mubarak Shah

In this paper, we argue that OOD samples can be detected more easily if the training data is embedded into a low-dimensional space, such that the embedded training samples lie on a union of 1-dimensional subspaces.

Bayesian Inference Out-of-Distribution Detection +2