C3: Cross-instance guided Contrastive Clustering

• URL: http://arxiv.org/abs/2211.07136v4
• Date: Fri, 1 Sep 2023 04:27:44 GMT
• Title: C3: Cross-instance guided Contrastive Clustering
• Authors: Mohammadreza Sadeghi, Hadi Hojjati, Narges Armanfard
• Abstract summary: Clustering is the task of gathering similar data samples into clusters without using any predefined labels. We propose a novel contrastive clustering method, Cross-instance guided Contrastive Clustering (C3) Our proposed method can outperform state-of-the-art algorithms on benchmark computer vision datasets.
• Score: 8.953252452851862
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clustering is the task of gathering similar data samples into clusters without using any predefined labels. It has been widely studied in machine learning literature, and recent advancements in deep learning have revived interest in this field. Contrastive clustering (CC) models are a staple of deep clustering in which positive and negative pairs of each data instance are generated through data augmentation. CC models aim to learn a feature space where instance-level and cluster-level representations of positive pairs are grouped together. Despite improving the SOTA, these algorithms ignore the cross-instance patterns, which carry essential information for improving clustering performance. This increases the false-negative-pair rate of the model while decreasing its true-positive-pair rate. In this paper, we propose a novel contrastive clustering method, Cross-instance guided Contrastive Clustering (C3), that considers the cross-sample relationships to increase the number of positive pairs and mitigate the impact of false negative, noise, and anomaly sample on the learned representation of data. In particular, we define a new loss function that identifies similar instances using the instance-level representation and encourages them to aggregate together. Moreover, we propose a novel weighting method to select negative samples in a more efficient way. Extensive experimental evaluations show that our proposed method can outperform state-of-the-art algorithms on benchmark computer vision datasets: we improve the clustering accuracy by 6.6%, 3.3%, 5.0%, 1.3% and 0.3% on CIFAR-10, CIFAR-100, ImageNet-10, ImageNet-Dogs, and Tiny-ImageNet.

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