  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.
