  Generalized Category Discovery (GCD) aims to recognize both known and novel
categories from a set of unlabeled data, based on another dataset labeled with
only known categories. Without considering differences between known and novel
categories, current methods learn about them in a coupled manner, which can
hurt model's generalization and discriminative ability. Furthermore, the
coupled training approach prevents these models transferring category-specific
knowledge explicitly from labeled data to unlabeled data, which can lose
high-level semantic information and impair model performance. To mitigate above
limitations, we present a novel model called Decoupled Prototypical Network
(DPN). By formulating a bipartite matching problem for category prototypes, DPN
can not only decouple known and novel categories to achieve different training
targets effectively, but also align known categories in labeled and unlabeled
data to transfer category-specific knowledge explicitly and capture high-level
semantics. Furthermore, DPN can learn more discriminative features for both
known and novel categories through our proposed Semantic-aware Prototypical
Learning (SPL). Besides capturing meaningful semantic information, SPL can also
alleviate the noise of hard pseudo labels through semantic-weighted soft
assignment. Extensive experiments show that DPN outperforms state-of-the-art
models by a large margin on all evaluation metrics across multiple benchmark
datasets. Code and data are available at https://github.com/Lackel/DPN.
