Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution Detection

arXiv:2204.11041v3 Announce Type: replace
Abstract: Out-of-distribution (OOD) detection is essential to handle the distribution shifts between training and test scenarios. For a new in-distribution (ID) dataset, existing methods require retraining to capture the dataset-specific feature representation or data distribution. In this paper, we propose a deep generative models (DGM) based transferable OOD detection method, which is unnecessary to retrain on a new ID dataset. We design an image erasing strategy to equip exclusive conditional entropy distribution for each ID dataset, …

arxiv cs.cv detection distribution entropy type