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 …
J Yin, H Wu, S Sun - Information Fusion, 2023 - Elsevier
As an indispensable branch of unsupervised learning, deep clustering is rapidly emerging along with the growth of deep neural networks. Recently, contrastive learning paradigm has …
X Deng, D Huang, DH Chen, CD Wang, JH Lai - Pattern Recognition, 2023 - Elsevier
Deep clustering has attracted increasing attention in recent years due to its capability of joint representation learning and clustering via deep neural networks. In its latest developments …
H Zhong, J Wu, C Chen, J Huang… - Proceedings of the …, 2021 - openaccess.thecvf.com
Recently, some contrastive learning methods have been proposed to simultaneously learn representations and clustering assignments, achieving significant improvements. However …
Recent advances in self-supervised learning with instance-level contrastive objectives facilitate unsupervised clustering. However, a standalone datum is not perceiving the …
In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes. We exploit the …
Recent developed deep unsupervised methods allow us to jointly learn representation and cluster unlabelled data. These deep clustering methods% like DAC start with mainly focus …
J Chang, L Wang, G Meng… - Proceedings of the …, 2017 - openaccess.thecvf.com
Image clustering is a crucial but challenging task in machine learning and computer vision. Existing methods often ignore the combination between feature learning and clustering. To …
H Zhong, C Chen, Z Jin, XS Hua - arXiv preprint arXiv:2008.03030, 2020 - arxiv.org
Recently, many unsupervised deep learning methods have been proposed to learn clustering with unlabelled data. By introducing data augmentation, most of the latest …