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:由于在现实世界应用中安全部署现代神经网络结构方面发挥着关键作用,所以最近对传播(OOD)的探测工作引起了人们的注意。OOD探测器旨在区分在培训分布范围之外的样本,以避免对OOD数据的机器学习模型作出过于自信的预测。现有的探测器主要依靠登录、中间地貌空间、软马克分或重建损失,设法产生有希望的结果。然而,这些方法大多是为图像领域开发的。在这项研究中,我们提议建立一个基于重建的OOOD探测器,以便在雷达领域运作。我们的方法利用自动编码器及其潜在代表来探测OOD样品。我们建议分两分,其中将补齐的重建损失和根据每个补丁的潜在表现计算的能源价值纳入其中。我们通过使用60 GHz短程调频CW雷达而收集的数据集实现了90.72%的AUROC。实验表明,从AUROC和APRI的角度来看,我们的方法超越了基线(AE)和APRI的功能。我们的方法是用来探测 OOD-41的合适的K方法。我们还建议把6号模型和K-B用于适当的探测方法。</s>