Graph Barlow Twins: A self-supervised representation learning framework for graphs

2021-06-04 · Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla

The self-supervised learning (SSL) paradigm is an essential exploration area, which tries to eliminate the need for expensive data labeling. Despite the great success of SSL methods in computer vision and natural language processing, most of them employ contrastive learning objectives that require negative samples, which are hard to define. This becomes even more challenging in the case of graphs and is a bottleneck for achieving robust representations. To overcome such limitations, we propose a framework for self-supervised graph representation learning - Graph Barlow Twins, which utilizes a cross-correlation-based loss function instead of negative samples. Moreover, it does not rely on non-symmetric neural network architectures - in contrast to state-of-the-art self-supervised graph representation learning method BGRL. We show that our method achieves as competitive results as the best self-supervised methods and fully supervised ones while requiring fewer hyperparameters and substantially shorter computation time (ca. 30 times faster than BGRL).

📄 PDF Abstract BibTeX arXiv:2106.02466

Code (1)

pbielak/graph-barlow-twins 공식 구현 pytorch

Tasks

Contrastive Learning Graph Representation Learning Representation Learning Self-Supervised Learning

Methods 이 논문이 사용한 방법론

Contrastive Learning 설명 없음

Barlow Twins Barlow Twins is a self-supervised learning method that applies redundancy-reduction — a principle first proposed in neuroscience — to self supervised learning. The objective…

Audio Barlow Twins: Self-Supervised Audio Representation Learning

2022-09-28 · Jonah Anton, Harry Coppock, Pancham Shukla, Bjorn W. Schuller

The Barlow Twins self-supervised learning objective requires neither negative samples or asymmetric learning updates, achieving results on a par with the current state-of-the-art within Computer Vision. As such, we prese…

Environmental Sound Classification Event Detection Representation Learning Self-Supervised Learning

BarlowRL: Barlow Twins for Data-Efficient Reinforcement Learning

2023-08-08 · Omer Veysel Cagatan, Baris Akgun

This paper introduces BarlowRL, a data-efficient reinforcement learning agent that combines the Barlow Twins self-supervised learning framework with DER (Data-Efficient Rainbow) algorithm. BarlowRL outperforms both DER a…

reinforcement-learning Reinforcement Learning Self-Supervised Learning

DinoTwins: Combining DINO and Barlow Twins for Robust, Label-Efficient Vision Transformers

2025-08-24 · Michael Podsiadly, Brendon K Lay arxiv

Training AI models to understand images without costly labeled data remains a challenge. We combine two techniques--DINO (teacher-student learning) and Barlow Twins (redundancy reduction)--to create a model that learns b…

Self-Supervised Learning Semantic Segmentation

Guarding Barlow Twins Against Overfitting with Mixed Samples

2023-12-04 · Wele Gedara Chaminda Bandara, Celso M. de Melo, Vishal M. Patel

Self-supervised Learning (SSL) aims to learn transferable feature representations for downstream applications without relying on labeled data. The Barlow Twins algorithm, renowned for its widespread adoption and straight…

Contrastive Learning Self-Supervised Learning

On the Optimal Representation Efficiency of Barlow Twins: An Information-Geometric Interpretation

2025-10-13 · Di Zhang arxiv

Self-supervised learning (SSL) has achieved remarkable success by learning meaningful representations without labeled data. However, a unified theoretical framework for understanding and comparing the efficiency of diffe…

Self-Supervised Learning