VICReg (Variance-Invariance-Covariance Regularization) is 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. It inherits the model structure from Barlow Twins, 2022 changing the loss. Doing so allows the stabilization of the training and leads to performance improvements.


VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, 2022

This example can be run from the command line with:

python lightly/examples/pytorch/
import torch
import torchvision
from torch import nn

## The projection head is the same as the Barlow Twins one
from lightly.loss import VICRegLoss

## The projection head is the same as the Barlow Twins one
from lightly.loss.vicreg_loss import VICRegLoss
from lightly.models.modules import BarlowTwinsProjectionHead
from lightly.transforms.vicreg_transform import VICRegTransform

class VICReg(nn.Module):
    def __init__(self, backbone):
        self.backbone = backbone
        self.projection_head = BarlowTwinsProjectionHead(512, 2048, 2048)

    def forward(self, x):
        x = self.backbone(x).flatten(start_dim=1)
        z = self.projection_head(x)
        return z

resnet = torchvision.models.resnet18()
backbone = nn.Sequential(*list(resnet.children())[:-1])
model = VICReg(backbone)

device = "cuda" if torch.cuda.is_available() else "cpu"

transform = VICRegTransform(input_size=32)
dataset = torchvision.datasets.CIFAR10(
    "datasets/cifar10", download=True, transform=transform
# or create a dataset from a folder containing images or videos:
# dataset = LightlyDataset("path/to/folder", transform=transform)

dataloader =
criterion = VICRegLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.06)

print("Starting Training")
for epoch in range(10):
    total_loss = 0
    for (x0, x1), _ in dataloader:
        x0 =
        x1 =
        z0 = model(x0)
        z1 = model(x1)
        loss = criterion(z0, z1)
        total_loss += loss.detach()
    avg_loss = total_loss / len(dataloader)
    print(f"epoch: {epoch:>02}, loss: {avg_loss:.5f}")