Contrastive Clustering

09/21/2020
by   Yunfan Li, et al.
0

In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, the positive and negative instance pairs are constructed through data augmentations and then projected into a feature space. Therein, the instance- and cluster-level contrastive learning are respectively conducted in the row and column space by maximizing the similarities of positive pairs while minimizing those of negative ones. Our key observation is that the rows of the feature matrix could be regarded as soft labels of instances, and accordingly the columns could be further regarded as cluster representations. By simultaneously optimizing the instance- and cluster-level contrastive loss, the model jointly learns representations and cluster assignments in an end-to-end manner. Extensive experimental results show that CC remarkably outperforms 17 competitive clustering methods on six challenging image benchmarks. In particular, CC achieves an NMI of 0.705 (0.431) on the CIFAR-10 (CIFAR-100) dataset, which is an up to 19% (39%) performance improvement compared with the best baseline.

READ FULL TEXT
09/26/2021

Cluster Analysis with Deep Embeddings and Contrastive Learning

Unsupervised disentangled representation learning is a long-standing pro...
06/03/2021

You Never Cluster Alone

Recent advances in self-supervised learning with instance-level contrast...
07/24/2021

Clustering by Maximizing Mutual Information Across Views

We propose a novel framework for image clustering that incorporates join...
07/08/2022

Few-Example Clustering via Contrastive Learning

We propose Few-Example Clustering (FEC), a novel algorithm that performs...
12/30/2021

Contrastive Fine-grained Class Clustering via Generative Adversarial Networks

Unsupervised fine-grained class clustering is practical yet challenging ...
09/28/2022

Efficient block contrastive learning via parameter-free meta-node approximation

Contrastive learning has recently achieved remarkable success in many do...
06/17/2021

Prototypical Graph Contrastive Learning

Graph-level representations are critical in various real-world applicati...