Papers › VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Adrien Bardes, Jean Ponce, Yann Lecun
Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image. A trivial solution is obtained when the encoder outputs constant vectors. This collapse problem is often avoided through implicit biases in the learning architecture, that often lack a clear justification or interpretation. In this paper, we introduce VICReg (Variance-Invariance-Covariance Regularization), a method that explicitly avoids the collapse problem with a simple regularization term on the variance of the embeddings along each dimension individually. VICReg combines the variance term with a decorrelation mechanism based on redundancy reduction and covariance regularization, and achieves results on par with the state of the art on several downstream tasks. In addition, we show that incorporating our new variance term into other methods helps stabilize the training and leads to performance improvements.
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Code
Syntology Ran 11 of 17 code samples harvested from 4 repositories linked to this paper; 6 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract; 3 ran · our draft was wrong; 2 ran · fixture could not drive it; 4 ran with no contract checked.
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Code Syntology ran Syntology
17 samples harvested; 11 ran; 1 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Self-Supervised Image Classification | ImageNet | VICReg (ResNet50) | Number of Params | 24M | #90 of 144 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | VICReg (ResNet50) | Top 1 Accuracy | 73.2 | #90 of 144 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | VICReg (ResNet50) | Top 5 Accuracy | 91.1 | #90 of 144 | Archive leaderboard | report |
| Semi-Supervised Image Classification | ImageNet - 1% labeled data | VICREG (Resnet-50) | Top 1 Accuracy | 54.8% | #47 of 65 | Archive leaderboard | report |
| Semi-Supervised Image Classification | ImageNet - 1% labeled data | VICREG (Resnet-50) | Top 5 Accuracy | 79.4% | #47 of 65 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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