Detecting out-of-distribution (OOD) inputs during the inference stage is crucial for deploying neural networks in the real world. Previous methods commonly relied on the output of a network derived from the highly activated feature map. In this study, we first revealed that a norm of the feature map obtained from the other block than the last block can be a better indicator of OOD detection. Motivated by this, we propose a simple framework consisting of FeatureNorm: a norm of the feature map and NormRatio: a ratio of FeatureNorm for ID and OOD to measure the OOD detection performance of each block. In particular, to select the block that provides the largest difference between FeatureNorm of ID and FeatureNorm of OOD, we create Jigsaw puzzle images as pseudo OOD from ID training samples and calculate NormRatio, and the block with the largest value is selected. After the suitable block is selected, OOD detection with the FeatureNorm outperforms other OOD detection methods by reducing FPR95 by up to 52.77% on CIFAR10 benchmark and by up to 48.53% on ImageNet benchmark. We demonstrate that our framework can generalize to various architectures and the importance of block selection, which can improve previous OOD detection methods as well.
翻译:在推论阶段检测分配外(OOOD)的输入对于在现实世界部署神经网络至关重要。 以往的方法通常依赖于来自高度激活地貌地图的网络输出。 在本研究中,我们首先发现,从另一块而不是最后一个块获得的地貌地图规范可以更好地显示OOD的检测。 我们为此提出一个简单框架,由地物Norm和NormRatio组成:地物图和NormRatio的规范:ID和OOD的地物Norm特性比,以测量每个街区的OOOOD探测性能。 特别是选择一个块,提供ID的地物Norm和OOOD特性Norm之间最大差异的区块。 我们从另一块获得的地物图图图图图图图图图解图解图解图解图。 在选定适当的区块后,OODOD的探测比超过OOD的其他方法,通过将FPR95降低到52.77%。 CIFAR10标准图图解图解图解基准和CFAR10基准图解图图图图图图图解图象基准,可以改进为48。