In today's interconnected world, achieving reliable out-of-distribution (OOD) detection poses a significant challenge for machine learning models. While numerous studies have introduced improved approaches for multi-class OOD detection tasks, the investigation into multi-label OOD detection tasks has been notably limited. We introduce Spectral Normalized Joint Energy (SNoJoE), a method that consolidates label-specific information across multiple labels through the theoretically justified concept of an energy-based function. Throughout the training process, we employ spectral normalization to manage the model's feature space, thereby enhancing model efficacy and generalization, in addition to bolstering robustness. Our findings indicate that the application of spectral normalization to joint energy scores notably amplifies the model's capability for OOD detection. We perform OOD detection experiments utilizing PASCAL-VOC as the in-distribution dataset and ImageNet-22K or Texture as the out-of-distribution datasets. Our experimental results reveal that, in comparison to prior top performances, SNoJoE achieves 11% and 54% relative reductions in FPR95 on the respective OOD datasets, thereby defining the new state of the art in this field of study.

翻译：在当今互联互通的世界中，实现可靠的分布外（OOD）检测对机器学习模型而言是一项重大挑战。尽管已有大量研究提出了改进多分类OOD检测任务的方法，但对多标签OOD检测任务的探索却显著有限。我们提出谱归一化联合能量（SNoJoE）方法，该方法通过基于能量函数的理论依据，整合了跨多个标签的标签特定信息。在训练过程中，我们采用谱归一化来管理模型的特征空间，从而在增强模型鲁棒性的同时，提升模型效能和泛化能力。我们的研究结果表明，将谱归一化应用于联合能量分数可显著增强模型的OOD检测能力。我们使用PASCAL-VOC作为分布内数据集，ImageNet-22K或Texture作为分布外数据集进行OOD检测实验。实验结果显示，与先前的最优性能相比，SNoJoE在相应OOD数据集上的FPR95分别相对降低了11%和54%，从而在该研究领域定义了新的最佳水平。