The findings on open-set recognition (OSR) show that models trained on classification datasets are capable of detecting unknown classes not encountered during the training process. Specifically, after training, the learned representations of known classes dissociate from the representations of the unknown class, facilitating OSR. In this paper, we investigate this emergent phenomenon by examining the relationship between the Jacobian norm of representations and the inter/intra-class learning dynamics. We provide a theoretical analysis, demonstrating that intra-class learning reduces the Jacobian norm for known class samples, while inter-class learning increases the Jacobian norm for unknown samples, even in the absence of direct exposure to any unknown sample. Overall, the discrepancy in the Jacobian norm between the known and unknown classes enables OSR. Based on this insight, which highlights the pivotal role of inter-class learning, we devise a marginal one-vs-rest (m-OvR) loss function that promotes strong inter-class separation. To further improve OSR performance, we integrate the m-OvR loss with additional strategies that maximize the Jacobian norm disparity. We present comprehensive experimental results that support our theoretical observations and demonstrate the efficacy of our proposed OSR approach.

翻译：开集识别（OSR）的研究表明，在分类数据集上训练的模型能够检测训练过程中未出现过的未知类别。具体而言，训练后的已知类别表征会与未知类别表征发生分离，从而促进OSR的实现。本文通过探究表征的雅可比范数与类别内/类别间学习动力学之间的关系，对这种涌现现象进行了深入研究。我们给出理论分析，证明类别内学习会降低已知类别样本的雅可比范数，而类别间学习则会提高未知类别样本的雅可比范数——即便在从未接触任何未知样本的情况下也是如此。总体而言，已知类别与未知类别之间雅可比范数的差异使得OSR成为可能。基于这一揭示类别间学习关键作用的洞见，我们设计了一种促进强类别间分离的边际一对其余（m-OvR）损失函数。为了进一步提升OSR性能，我们将m-OvR损失与最大化雅可比范数差异的附加策略相结合。我们提供了全面的实验结果，既验证了理论分析，也证明了所提OSR方法的有效性。