- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://arxiv.org/abs/2105.04906) - [Self Supervised](Self%20Supervised.md) - based on maximizing the agreement between [Embedding](Embedding.md) vectors from different views of the same image - rivial solution is obtained when the encoder outputs constant vectors - [Mode Collapse](Mode%20Collapse.md) is often avoided through implicit biases - explicitly avoids the collapse problem with a simple [Regularization](Regularization.md) term on the variance of the embeddings along each dimension individually - triple objective: learning invariance to different views with a invariance term, avoiding collapse of the representations with a variance preservation term, and maximizing the information content of the representation with a [Covariance](Covariance.md) [Regularization](Regularization.md) term - [Bias Vs Variance](Bias%20Vs%20Variance) - combines the variance term with a decorrelation mechanism based on redundancy reduction and [Covariance](Covariance.md) [Regularization](Regularization.md) - does not require the [Embedding](Embedding.md) branches to be identical or even similar