In open-set recognition, existing methods generally learn statically fixed decision boundaries using known classes to reject unknown classes. Though they have achieved promising results, such decision boundaries are evidently insufficient for universal unknown classes in dynamic and open scenarios as they can potentially appear at any position in the feature space. Moreover, these methods just simply reject unknown class samples during testing without any effective utilization for them. In fact, such samples completely can constitute the true instantiated representation of the unknown classes to further enhance the model's performance. To address these issues, this paper proposes a novel dynamic against dynamic idea, i.e., dynamic method against dynamic changing open-set world, where an open-set self-learning (OSSL) framework is correspondingly developed. OSSL starts with a good closed-set classifier trained by known classes and utilizes available test samples for model adaptation during testing, thus gaining the adaptability to changing data distributions. In particular, a novel self-matching module is designed for OSSL, which can achieve the adaptation in automatically identifying known class samples while rejecting unknown class samples which are further utilized to enhance the discriminability of the model as the instantiated representation of unknown classes. Our method establishes new performance milestones respectively in almost all standard and cross-data benchmarks.

翻译：在开放集识别中，现有方法通常通过已知类别学习静态固定的决策边界来拒绝未知类别。尽管这些方法取得了显著成果，但在动态开放场景下，由于未知类别可能出现在特征空间的任意位置，此类决策边界显然无法应对普适性未知类别。此外，这些方法在测试阶段仅简单拒绝未知类样本而未对其进行有效利用。实际上，这类样本完全可以构成未知类别的真实实例化表征，从而进一步增强模型的性能。针对上述问题，本文提出了一种新颖的动态对动态思想——即用动态方法应对动态变化的开放集世界，并据此开发了开放集自学习（OSSL）框架。OSSL以已知类别训练的良好闭集分类器为起点，在测试阶段利用可用测试样本进行模型自适应，从而获得对数据分布变化的适应能力。特别地，我们为OSSL设计了新颖的自匹配模块，该模块可在自动识别已知类样本并拒绝未知类样本的同时，将这些被拒绝的未知类样本作为未知类别的实例化表征，进一步提升模型的判别能力。我们的方法在几乎所有标准基准和跨数据基准上分别树立了新的性能里程碑。