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arxiv_cv 95% Match Research Paper Machine Learning Researchers,Computer Vision Engineers,Data Scientists,AI Researchers 1 week ago

Beyond Augmentation: Leveraging Inter-Instance Relation in Self-Supervised Representation Learning

graph-neural-networks › graph-learning
📄 Abstract

Abstract: This paper introduces a novel approach that integrates graph theory into self-supervised representation learning. Traditional methods focus on intra-instance variations generated by applying augmentations. However, they often overlook important inter-instance relationships. While our method retains the intra-instance property, it further captures inter-instance relationships by constructing k-nearest neighbor (KNN) graphs for both teacher and student streams during pretraining. In these graphs, nodes represent samples along with their latent representations. Edges encode the similarity between instances. Following pretraining, a representation refinement phase is performed. In this phase, Graph Neural Networks (GNNs) propagate messages not only among immediate neighbors but also across multiple hops, thereby enabling broader contextual integration. Experimental results on CIFAR-10, ImageNet-100, and ImageNet-1K demonstrate accuracy improvements of 7.3%, 3.2%, and 1.0%, respectively, over state-of-the-art methods. These results highlight the effectiveness of the proposed graph based mechanism. The code is publicly available at https://github.com/alijavidani/SSL-GraphNNCLR.
Authors (3)
Ali Javidani
Babak Nadjar Araabi
Mohammad Amin Sadeghi
Submitted
October 25, 2025
arXiv Category
cs.CV
IEEE Signal Processing Letters, vol. 32, pp. 3730-3734, 2025
arXiv PDF

Key Contributions

This paper introduces a novel self-supervised representation learning approach that leverages inter-instance relationships by constructing k-nearest neighbor (KNN) graphs. By using Graph Neural Networks (GNNs) for message passing across these graphs, it enables richer contextual integration beyond traditional intra-instance augmentations, leading to improved representation quality and downstream task accuracy.

Business Value

Enhances the performance of machine learning models by learning richer, more context-aware representations from unlabeled data. This can lead to more accurate AI systems in various applications without the need for extensive labeled datasets.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

Moderate. Requires implementing GNNs and graph construction, which adds complexity to the training pipeline. However, the resulting representations can be used with standard downstream models.

Limitations Addressed

Traditional SSL methods focus primarily on intra-instance variations (augmentations).,Overlooking the importance of relationships between different instances.,Limited contextual integration in learned representations.

Performance Gains

7.3% accuracy improvement on CIFAR-10,3.2% accuracy improvement on ImageNet-100,1.0% accuracy improvement on ImageNet-1K

Technical Tags

self-supervised learningrepresentation learninggraph neural networksinter-instance relationsKNN graphsmessage passingcontextual integrationteacher-student modeldata augmentationfeature refinement

Research Topics

Graph-based Representation LearningSelf-Supervised Learning EnhancementsInter-Instance Relationship ModelingContextual Feature IntegrationImproving Representation Quality

Methods & Architectures

KNN Graph ConstructionGraph Neural Networks (GNNs)Message PassingTeacher-Student FrameworkData AugmentationRepresentation Refinement Graph Neural Networks (GNNs)Teacher-Student Model

Applications & Tasks

Computer Vision Machine Learning Data Analysis Overlooking inter-instance relationships in SSLLimited contextual integration in representation learningImproving accuracy and representation quality Self-supervised representation learningFeature extractionDownstream task performance improvement

Datasets & Benchmarks

Datasets
CIFAR-10, ImageNet-100, ImageNet-1K
Benchmarks
CIFAR-10: 7.3% accuracy improvement • ImageNet-100: 3.2% accuracy improvement • ImageNet-1K: 1.0% accuracy improvement
Accuracy

Related Fields

Graph Neural NetworksSelf-Supervised LearningRepresentation LearningComputer VisionMachine LearningDeep Learning

Keywords

Self-Supervised LearningRepresentation LearningGraph Neural NetworksGNNInter-Instance RelationsKNN GraphMessage PassingContextual IntegrationTeacher-StudentDeep LearningComputer VisionUnsupervised LearningFeature Learning

Academic Context

#Graph-based Representation Learning#Self-Supervised Learning Enhancements#Inter-Instance Relationship Modeling#Contextual Feature Integration#Improving Representation Quality

Commercial Potential

Potential Products
Improved foundation models for computer vision tasksMore robust feature extractors for various data typesEnhanced AI systems requiring rich feature representations
Target Industries
TechnologyHealthcare (medical imaging)Retail (image analysis)Automotive (perception)
Use Case Examples
Pre-training models for image classification with better generalizationDeveloping more accurate object detection systemsImproving facial recognition accuracy by capturing relational features
Competitive Edge
Offers an advancement over standard self-supervised methods by incorporating graph-based inter-instance relationships, leading to richer representations and improved performance on benchmark datasets.
Market Opportunity
Growing demand for efficient representation learning techniques, especially in unsupervised and semi-supervised settings.
Revenue Models
Licensing of improved models or training methodologiesintegration into AI platforms.

Resource Requirements

Compute Needs
Requires significant computational resources for training GNNs on large datasets, especially with message passing over graphs.
Data Requirements
Can leverage large unlabeled datasets, but performance is sensitive to the quality and structure of the constructed graphs.
Deployment Constraints
Computational overhead of GNNs,Graph construction complexity
Scalability
Scalability depends on the efficiency of the GNN implementation and graph construction. Techniques for handling large graphs would be necessary for massive datasets.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for integration into advanced ML libraries and platforms.

Patent Potential

Moderate, for the specific graph construction and message-passing mechanisms applied to SSL.

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