Abstract
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.
Abstract (translated)
在开集识别中，现有方法通常使用已知类别来学习静态固定的决策边界以拒绝未知类别。虽然它们取得了很好的效果，但显然这些决策边界对于动态和开放场景中的通用未知类是不够的，因为它们可能出现在特征空间的任何位置。此外，这些方法在测试过程中只是简单地拒绝未知类样本，而没有对这些样本进行有效的利用。实际上，这些样本完全可以构成未知类的真实实例表示，进一步增强模型的性能。为解决这些问题，本文提出了一种新颖的动态对抗动态的想法，即动态方法对抗动态变化开集世界，相应地开发了一个自适应学习（OSSL）框架。OSSL从已知类别的良好闭合类ifier开始训练，并在测试过程中利用可用的测试样本进行模型适应，从而获得了适应变化数据分布的特性。特别地，为OSSL设计了一个新颖的自匹配模块，可以在自动识别已知类样本的同时拒绝未知类样本，作为未知类类别的实例表示，进一步加强模型的区分性。我们的方法在几乎所有标准和跨数据基准测试中都建立了新的性能里程碑。
URL
https://arxiv.org/abs/2404.17830
