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.

通过应用能量函数的理论概念和谱归一化方法，本研究提出了一种名为 SNoJoE 的能量函数，用于多标签异常检测任务，实验结果表明相对于先前方法，SNoJoE 在异常检测方面取得了显著改进，定义了该研究领域的新的最佳方法。