Continual learning (CL) addresses the problem of catastrophic forgetting in neural networks, which occurs when a trained model tends to overwrite previously learned information, when presented with a new task. CL aims to instill the lifelong learning characteristic of humans in intelligent systems, making them capable of learning continuously while retaining what was already learned. Current CL problems involve either learning new domains (domain-incremental) or new and previously unseen classes (class-incremental). However, general learning processes are not just limited to learning information, but also refinement of existing information. In this paper, we define CLEO - Continual Learning of Evolving Ontologies, as a new incremental learning setting under CL to tackle evolving classes. CLEO is motivated by the need for intelligent systems to adapt to real-world ontologies that change over time, such as those in autonomous driving. We use Cityscapes, PASCAL VOC, and Mapillary Vistas to define the task settings and demonstrate the applicability of CLEO. We highlight the shortcomings of existing CIL methods in adapting to CLEO and propose a baseline solution, called Modelling Ontologies (MoOn). CLEO is a promising new approach to CL that addresses the challenge of evolving ontologies in real-world applications. MoOn surpasses previous CL approaches in the context of CLEO.

翻译：持续学习（CL）旨在解决神经网络中的灾难性遗忘问题，即当训练好的模型面对新任务时，往往会覆盖先前已学习的信息。CL的目标是在智能系统中注入人类终身学习的特性，使其能够持续学习的同时保留已掌握的知识。当前的CL问题主要涉及学习新领域（领域增量）或学习新的、先前未见过的类别（类别增量）。然而，一般的学习过程不仅限于学习新信息，还包括对现有信息的精炼。本文中，我们定义了CLEO——演化本体的持续学习，作为CL框架下处理演化类别的新增量学习设定。CLEO的提出源于智能系统需要适应现实世界中随时间变化的本体，例如自动驾驶中的本体。我们使用Cityscapes、PASCAL VOC和Mapillary Vistas数据集来定义任务设定，并展示CLEO的适用性。我们指出了现有CIL方法在适应CLEO方面的不足，并提出了一种名为建模本体（MoOn）的基线解决方案。CLEO是一种有前景的持续学习新方法，能够应对现实应用中本体演化的挑战。在CLEO的背景下，MoOn超越了以往的CL方法。