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, which determines the discrepancy of DGM's posteriori ucertainty distribution on different ID datasets. Owing to the powerful representation capacity of convolutional neural networks, the proposed model trained on complex dataset can capture the above discrepancy between ID datasets without retraining and thus achieve transferable OOD detection. We validate the proposed method on five datasets and verity that ours achieves comparable performance to the state-of-the-art group based OOD detection methods that need to be retrained to deploy on new ID datasets. Our code is available at https://github.com/oOHCIOo/CETOOD.

翻译：分布外（OOD）检测对于处理训练与测试场景之间的分布偏移至关重要。针对新的分布内（ID）数据集，现有方法需要重新训练以捕获数据集特定的特征表示或数据分布。本文提出一种基于深度生成模型（DGM）的可迁移OOD检测方法，该方法无需在新ID数据集上重新训练。我们设计了一种图像擦除策略，为每个ID数据集赋予专属的条件熵分布，该分布决定了DGM在不同ID数据集上的后验不确定性分布差异。得益于卷积神经网络强大的表示能力，在复杂数据集上训练的模型无需重新训练即可捕获ID数据集之间的上述差异，从而实现可迁移的OOD检测。我们在五个数据集上验证了所提方法，证明其性能与需要重新训练才能部署于新ID数据集的最先进基于组的OOD检测方法相当。我们的代码已开源至https://github.com/oOHCIOo/CETOOD。