In the quest for unveiling novel categories at test time, we confront the inherent limitations of traditional supervised recognition models that are restricted by a predefined category set. While strides have been made in the realms of self-supervised and open-world learning towards test-time category discovery, a crucial yet often overlooked question persists: what exactly delineates a category? In this paper, we conceptualize a category through the lens of optimization, viewing it as an optimal solution to a well-defined problem. Harnessing this unique conceptualization, we propose a novel, efficient and self-supervised method capable of discovering previously unknown categories at test time. A salient feature of our approach is the assignment of minimum length category codes to individual data instances, which encapsulates the implicit category hierarchy prevalent in real-world datasets. This mechanism affords us enhanced control over category granularity, thereby equipping our model to handle fine-grained categories adeptly. Experimental evaluations, bolstered by state-of-the-art benchmark comparisons, testify to the efficacy of our solution in managing unknown categories at test time. Furthermore, we fortify our proposition with a theoretical foundation, providing proof of its optimality. Our code is available at https://github.com/SarahRastegar/InfoSieve.

翻译：在测试时揭示新类别的探索中，我们面临传统监督识别模型因预定义类别集而固有的局限性。尽管自监督学习和开放世界学习领域在测试时类别发现方面取得了进展，但仍存在一个关键却常被忽视的问题：类别究竟如何界定？本文从优化视角重新审视类别概念，将其视为良好定义问题的最优解。基于这一独特概念，我们提出一种新颖、高效且自监督的方法，能够在测试时发现未知类别。该方法的核心是为每个数据实例分配最小长度类别编码，从而捕捉真实数据集中普遍存在的隐式类别层级结构。这种机制赋予我们对类别粒度的增强控制能力，使模型能够精准处理细粒度类别。通过最新基准测试的对比实验验证，本方案在测试时未知类别管理中的有效性得到证实。此外，我们为命题提供了理论支撑，并给出了最优性证明。代码开源于 https://github.com/SarahRastegar/InfoSieve。