Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

The similarity among samples and the discrepancy among clusters are two crucial aspects
of image clustering. However, current deep clustering methods suffer from inaccurate …

Investigating active learning, we focus on the relation between the number of labeled
examples (budget size), and suitable querying strategies. Our theoretical analysis shows a …

This paper presents FedX, an unsupervised federated learning framework. Our model learns
unbiased representation from decentralized and heterogeneous local data. It employs a two …

Several unsupervised image segmentation approaches have been proposed which
eliminate the need for dense manually-annotated segmentation masks; current models …

Federated learning enables learning from decentralized data sources without compromising
privacy, which makes it a crucial technique. However, it is vulnerable to model poisoning …

Session-based Recommendation aims at predicting the next interacted item based on short
anonymous behavior sessions. However, existing solutions neglect to model two inherent …

We propose a novel framework for image clustering that incorporates joint representation
learning and clustering. Our method consists of two heads that share the same backbone …

Recent advances in self-supervised learning with instance-level contrastive objectives
facilitate unsupervised clustering. However, a standalone datum is not perceiving the …

This paper proposes to perform online clustering by conducting twin contrastive learning
(TCL) at the instance and cluster level. Specifically, we find that when the data is projected …