We study the new task of class-incremental Novel Class Discovery (class-iNCD), which refers to the problem of discovering novel categories in an unlabelled data set by leveraging a pre-trained model that has been trained on a labelled data set containing disjoint yet related categories. Apart from discovering novel classes, we also aim at preserving the ability of the model to recognize previously seen base categories. Inspired by rehearsal-based incremental learning methods, in this paper we propose a novel approach for class-iNCD which prevents forgetting of past information about the base classes by jointly exploiting base class feature prototypes and feature-level knowledge distillation. We also propose a self-training clustering strategy that simultaneously clusters novel categories and trains a joint classifier for both the base and novel classes. This makes our method able to operate in a class-incremental setting. Our experiments, conducted on three common benchmarks, demonstrate that our method significantly outperforms state-of-the-art approaches. Code is available at https://github.com/OatmealLiu/class-iNCD
翻译:我们研究新类新发现类的新任务,即利用在含有脱节但相关的类的标签数据集上受过训练的经过预先训练的模型,在未贴标签的数据集中发现新类。我们除了研究新类外,还旨在保持该模型识别以前所见基类的能力。在以彩排为基础的增量学习方法的启发下,我们在本文件中提议对类新发现类采取新颖的办法,通过联合开发基础类特征原型和特征级知识蒸馏,防止忘记关于基础类的过去信息。我们还提议了一项自我培训组群战略,同时将新类和新类同时分组,并培训一个联合分类师。这使我们的方法能够在等级分类环境中运作。我们根据三个共同基准进行的实验表明,我们的方法大大超越了艺术状态的方法。代码见https://github.com/OatmealLu/clasul-iNCD。