Out-of-distribution (OOD) detection is a critical requirement for the deployment of deep neural networks. This paper introduces the HEAT model, a new post-hoc OOD detection method estimating the density of in-distribution (ID) samples using hybrid energy-based models (EBM) in the feature space of a pre-trained backbone. HEAT complements prior density estimators of the ID density, e.g. parametric models like the Gaussian Mixture Model (GMM), to provide an accurate yet robust density estimation. A second contribution is to leverage the EBM framework to provide a unified density estimation and to compose several energy terms. Extensive experiments demonstrate the significance of the two contributions. HEAT sets new state-of-the-art OOD detection results on the CIFAR-10 / CIFAR-100 benchmark as well as on the large-scale Imagenet benchmark. The code is available at: https://github.com/MarcLafon/heatood.

翻译：外分布检测是深度神经网络部署中的关键需求。本文提出HEAT模型，这是一种新的后处理方法，通过在预训练骨干网络的特征空间中应用混合能量模型来估计内分布样本的密度。HEAT补充了现有的内分布密度估计方法（如高斯混合模型等参数化模型），能够提供准确且稳健的密度估计。第二项贡献在于利用能量模型框架实现统一的密度估计，并组合多个能量项。大量实验验证了这两项贡献的重要性。HEAT在CIFAR-10/CIFAR-100基准测试以及大规模ImageNet基准测试上均取得了新的最优外分布检测结果。代码开源地址：https://github.com/MarcLafon/heatood。