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 to identify both known and novel categories in unlabeled data by leveraging knowledge from labeled datasets.

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

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 …

Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention. Given a dataset that includes both labelled and unlabelled images, GCD …

Generalized Category Discovery (GCD) typically relies on the pre-trained Vision Transformer (ViT) to extract features from a global receptive field, followed by contrastive learning to …

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 …

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 …

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 …

Generalized Category Discovery (GCD) typically relies on the pre-trained Vision Transformer (ViT) to extract features from a global receptive field, followed by contrastive learn-ing to …