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Barlow Twins

71 papers tagged archive 2025-07-28

Introduced by Jure Zbontar et al. in Barlow Twins: Self-Supervised Learning via Redundancy Reduction

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Barlow Twins is a self-supervised learning method that applies redundancy-reduction — a principle first proposed in neuroscience — to self supervised learning. The objective function measures the cross-correlation matrix between the embeddings of two identical networks fed with distorted versions of a batch of samples, and tries to make this matrix close to the identity. This causes the embedding vectors of distorted version of a sample to be similar, while minimizing the redundancy between the components of these vectors. Barlow Twins does not require large batches nor asymmetry between the network twins such as a predictor network, gradient stopping, or a moving average on the weight updates. Intriguingly it benefits from very high-dimensional output vectors.

PaperSource

Papers archive 2025-07-28

30 shown of 71, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 105 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Self-Supervised Learning54
Representation Learning23
Contrastive Learning16
Transfer Learning8
Image Classification6
Semantic Segmentation5
image-classification5
Active Learning3
Benchmarking3
Disentanglement3
Domain Adaptation3
Image Segmentation3
Language Modelling3
Activity Recognition2
Autonomous Driving2
Autonomous Vehicles2
Clustering2
Continual Learning2
Data Augmentation2
Decoder2

Usage over time archive 2025-07-28

Papers per year tagged with Barlow Twins: 2021 to 2025, peak 22 22 0 2021: 11 papers 2021 2022: 16 papers 2022 2023: 19 papers 2023 2024: 22 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (71 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Self-Supervised Learning

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