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 …

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 …

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

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

Given a dataset that includes both labelled and unlabelled images, GCD aims to categorize all images in the unlabelled subset, regardless of whether they belong to known or unknown classes.

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 …

Abstract Generalized Category Discovery (GCD) aims to identify a mix of known and novel categories within unlabeled data sets, providing a more realistic setting for image recogni-tion. Essentially, GCD …

Generalized Category Discovery (GCD) is a classification task that aims to classify both base and novel classes in unlabeled images, using knowledge from a labeled dataset. In GCD, previous research …