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This paper tackles the problem of novel category discovery (NCD), which aimsto discriminate unknown categories in large-scale image collections. The NCDtask is challenging due to the closeness to the real-world scenarios, where wehave only encountered some partial classes and images. Unlike other works onthe NCD, we leverage the prototypes to emphasize the importance of categorydiscrimination and alleviate the issue of missing annotations of novel classes.Concretely, we propose a novel adaptive prototype learning method consisting oftwo main stages: prototypical representation learning and prototypicalself-training. In the first stage, we obtain a robust feature extractor, whichcould serve for all images with base and novel categories. This ability ofinstance and category discrimination of the feature extractor is boosted byself-supervised learning and adaptive prototypes. In the second stage, weutilize the prototypes again to rectify offline pseudo labels and train a finalparametric classifier for category clustering. We conduct extensive experimentson four benchmark datasets and demonstrate the effectiveness and robustness ofthe proposed method with state-of-the-art performance.

Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning