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 …

In recent years, with the great success of deep learning and especially deep unsupervised
learning, many deep architectural clustering methods, collectively known as deep clustering …

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 …

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

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

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

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 …

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

In the realm of Earth observation and remote sensing data analysis, the advancement of
hyperspectral imaging (HSI) classification technology is of paramount importance …