Recent advances in deep learning have significantly improved the performance of various computer vision applications. However, discovering novel categories in an incremental learning scenario remains a challenging problem due to the lack of prior knowledge about the number and nature of new categories. Existing methods for novel category discovery are limited by their reliance on labeled datasets and prior knowledge about the number of novel categories and the proportion of novel samples in the batch. To address the limitations and more accurately reflect real-world scenarios, in this paper, we propose a novel unsupervised class incremental learning approach for discovering novel categories on unlabeled sets without prior knowledge. The proposed method fine-tunes the feature extractor and proxy anchors on labeled sets, then splits samples into old and novel categories and clusters on the unlabeled dataset. Furthermore, the proxy anchors-based exemplar generates representative category vectors to mitigate catastrophic forgetting. Experimental results demonstrate that our proposed approach outperforms the state-of-the-art methods on fine-grained datasets under real-world scenarios.

翻译：深度学习的最新进展显著提升了多种计算机视觉应用的性能。然而，在增量学习场景中，由于缺乏关于新类别数量和性质先验知识，发现新类别仍是一个具有挑战性的问题。现有的新类别发现方法受限于其对标记数据集的依赖，以及对新类别数量和批次中新样本比例的预先了解。为克服这些限制并更准确地反映现实场景，本文提出一种新颖的无监督类增量学习方法，无需先验知识即可在未标记数据集上发现新类别。该方法在标记数据集上微调特征提取器和代理锚点，随后将未标记数据集中的样本划分为旧类别和新类别并进行聚类。此外，基于代理锚点的示例生成具有代表性的类别向量，以缓解灾难性遗忘。实验结果表明，在真实场景下的细粒度数据集上，所提方法优于现有最先进方法。