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 classify inputs into both known and novel categories, a task crucial for open-world scientific discoveries. However, current GCD methods …

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 …

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

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 …

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 …

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 …

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. …

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 …

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