Discovering novel concepts from unlabelled data and in a continuous manner is an important desideratum of lifelong learners. In the literature such problems have been partially addressed under very restricted settings, where either access to labelled data is provided for discovering novel concepts (e.g., NCD) or learning occurs for a limited number of incremental steps (e.g., class-iNCD). In this work we challenge the status quo and propose a more challenging and practical learning paradigm called MSc-iNCD, where learning occurs continuously and unsupervisedly, while exploiting the rich priors from large-scale pre-trained models. To this end, we propose simple baselines that are not only resilient under longer learning scenarios, but are surprisingly strong when compared with sophisticated state-of-the-art methods. We conduct extensive empirical evaluation on a multitude of benchmarks and show the effectiveness of our proposed baselines, which significantly raises the bar.

翻译：从无标注数据中持续发现新概念是终身学习的重要目标。现有文献仅在严格受限条件下部分解决了该问题：要么依赖标注数据指导新概念发现（如NCD），要么仅支持有限增量步骤的学习（如class-iNCD）。本研究挑战现有范式，提出更具挑战性和实用性的学习范式MSc-iNCD，在持续无监督学习过程中充分利用大规模预训练模型的丰富先验知识。我们构建了简易基线方法，这些方法不仅能适应更长时间的学习场景，且与现有先进方法相比展现出惊人优势。通过多基准数据集的大量实证评估，我们证明了所提基线方法的有效性，显著提升了该领域的性能上限。