Continual Learning (CL) often relies on the availability of extensive annotated datasets, an assumption that is unrealistically time-consuming and costly in practice. We explore a novel paradigm termed name-only continual learning where time and cost constraints prohibit manual annotation. In this scenario, learners adapt to new category shifts using only category names without the luxury of annotated training data. Our proposed solution leverages the expansive and ever-evolving internet to query and download uncurated webly-supervised data for image classification. We investigate the reliability of our web data and find them comparable, and in some cases superior, to manually annotated datasets. Additionally, we show that by harnessing the web, we can create support sets that surpass state-of-the-art name-only classification that create support sets using generative models or image retrieval from LAION-5B, achieving up to 25% boost in accuracy. When applied across varied continual learning contexts, our method consistently exhibits a small performance gap in comparison to models trained on manually annotated datasets. We present EvoTrends, a class-incremental dataset made from the web to capture real-world trends, created in just minutes. Overall, this paper underscores the potential of using uncurated webly-supervised data to mitigate the challenges associated with manual data labeling in continual learning.

通过利用互联网中的未经整理的数据，本论文探讨了使用仅有类别名称而无标注训练数据的新模式 name-only continual learning，在持续学习中解决手动标注数据的挑战。通过使用网络数据创建支持集，我们展示了与使用生成模型或从LAION-5B进行图像检索创建支持集的最新模型相比，准确度提升了25%。由于使用未经整理的数据可以缓解手动数据标注的挑战，我们的方法在各种持续学习场景中始终表现出小幅度的性能差距。创造了一个用于捕捉实际趋势的类增量数据集 EvoTrends，仅需几分钟即可创建。

从分类到分类器：通过探索网络实现仅基于名称的持续学习