Generalized Category Discovery (GCD) aims to classify inputs into both known and novel categories, a task crucial for open-world scientific discoveries. However, current GCD methods are limited to …

The goal of Fed-GCD is to collaboratively train a generic GCD model under the privacy constraint, and then utilize it to discover novel categories in the unlabeled data on the server.

Generalized category discovery (GCD) aims at grouping unlabeled samples from known and unknown classes given labeled data of known classes.

Abstract Generalized Category Discovery (GCD) aims to classify in-puts into both known and novel categories, a task crucial for open-world scientific discoveries. However, current GCD methods are …

Abstract Given unlabelled datasets containing both old and new cat-egories, generalized category discovery (GCD) aims to ac-curately discover new classes while correctly classifying old classes. …

We thoroughly evaluate HypCD on public GCD benchmarks, by applying it to various baseline and state-of-the-art methods, consistently achieving significant improvements.

Generalized Category Discovery (GCD) aims to identify both known and novel categories in unlabeled data by leveraging knowledge from labeled datasets.

Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task which endeavors to cluster unlabeled samples from both novel and old classes leveraging some labeled …

GCD aims to learn a model capable of accurately classify-ing unlabelled samples from known categories while simul-taneously clustering those from unknown categories.

To address this issue, we introduce the novel paradigm of Domain Generalization in GCD (DG-GCD), where only source data is available for training, while the target domain--with a distinct data …