Out-of-distribution (OOD) detection recently has drawn attention due to its critical role in the safe deployment of modern neural network architectures in real-world applications. The OOD detectors aim to distinguish samples that lie outside the training distribution in order to avoid the overconfident predictions of machine learning models on OOD data. Existing detectors, which mainly rely on the logit, intermediate feature space, softmax score, or reconstruction loss, manage to produce promising results. However, most of these methods are developed for the image domain. In this study, we propose a novel reconstruction-based OOD detector to operate on the radar domain. Our method exploits an autoencoder (AE) and its latent representation to detect the OOD samples. We propose two scores incorporating the patch-based reconstruction loss and the energy value calculated from the latent representations of each patch. We achieve an AUROC of 90.72% on our dataset collected by using 60 GHz short-range FMCW Radar. The experiments demonstrate that, in terms of AUROC and AUPR, our method outperforms the baseline (AE) and the other state-of-the-art methods. Also, thanks to its model size of 641 kB, our detector is suitable for embedded usage.

翻译：离群样本检测（OOD检测）因其在现代神经网络架构安全部署于实际应用中的关键作用而备受关注。OOD检测器旨在区分训练分布之外的样本，以避免机器学习模型对OOD数据产生过度自信的预测。现有检测器主要依赖logit值、中间特征空间、softmax分数或重构损失等方法，并取得了显著成果。然而，这些方法大多针对图像领域开发。本研究提出一种基于重构的新型OOD检测器，应用于雷达领域。我们的方法利用自编码器及其潜在表征来检测OOD样本。我们提出两种评分指标：基于分块的重构损失，以及每个分块潜在表征计算得到的能量值。在采用60 GHz短程FMCW雷达采集的数据集上，我们的方法达到了90.72%的AUROC。实验表明，在AUROC和AUPR指标上，我们的方法优于基线自编码器及其他先进方法。此外，得益于仅641 kB的模型大小，该检测器适用于嵌入式场景。