Self-supervised learning from images: No negative pairs, no cluster-balancing

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Recognition Pub Date : 2024-10-18 DOI:10.1016/j.patcog.2024.111081
Jian-Ping Mei, Shixiang Wang, Miaoqi Yu
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Abstract

Learning with self-derived targets provides a non-contrastive method for unsupervised image representation learning, where the variety in targets is crucial. Recent work has achieved good performance by learning with targets obtained via cluster-balancing. However, the equal-cluster-size constraint becomes too restrictive for handling data with imbalanced categories or coming in small batches. In this paper, we propose a new clustering-based approach for non-contrastive image representation learning with no need for a particular architecture design or extra memory bank and no explicit constraints on cluster size. A key formulation is to learn embedding consistency and variable decorrelation in the cluster space by tweaking the batch-wise cross-correlation matrix towards an identity one. With this identitization loss incorporated, predicted cluster assignments of two randomly augmented views of the same image serve as targets for each other. We carried out comprehensive experimental studies of linear classification with learned representations of benchmark image datasets. Our results show that the proposed approach significantly outperforms state-of-the-art approaches and is more robust to class imbalance than those with cluster balancing.
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图像自监督学习无负对,无聚类平衡
利用自生成的目标进行学习为无监督图像表征学习提供了一种非对比方法,在这种方法中,目标的多样性至关重要。最近的研究通过聚类平衡获得的目标进行学习,取得了很好的效果。然而,在处理类别不平衡或小批量数据时,簇大小相等的约束变得过于严格。在本文中,我们提出了一种新的基于聚类的非对比图像表征学习方法,它不需要特定的架构设计或额外的内存库,也没有明确的聚类大小限制。其关键表述是,通过调整批次交叉相关矩阵,使其趋向于一个同一矩阵,从而学习聚类空间中的嵌入一致性和可变去相关性。有了这种识别损失,同一图像的两个随机增强视图的预测聚类分配就会成为彼此的目标。我们利用基准图像数据集的学习表示对线性分类进行了全面的实验研究。我们的结果表明,所提出的方法明显优于最先进的方法,而且与那些采用聚类平衡的方法相比,它对类不平衡具有更强的鲁棒性。
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来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
期刊最新文献
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