Batch normalization (BN) is a technique that standardizes the inputs to a neural network layer by adjusting and scaling activations to have zero mean and unit variance within each training batch. In self-supervised learning (SSL), where models learn from unlabeled data by creating proxy tasks (e.g., predicting image rotations or contrasting augmented views), BN helps stabilize training by reducing internal covariate shift—the change in layer input distributions during training. By normalizing activations, BN allows the model to use higher learning rates and converges faster, which is critical in SSL where training often starts with noisy, unstructured data. For example, in contrastive learning frameworks like SimCLR, BN is applied to the projection head (the final layers that map embeddings to a contrastive loss space) to ensure stable feature distributions during training.
However, BN in SSL requires careful implementation to avoid unintended shortcuts. Since BN computes statistics across a batch, it can leak information if the proxy task involves distinguishing between samples within the same batch. For instance, in some early SSL models, using BN in the projection head inadvertently allowed the model to exploit batch-level statistics to solve the task instead of learning meaningful features. This issue was addressed in frameworks like MoCo (Momentum Contrast), which avoids BN in the projection head and instead uses a momentum encoder—a slowly updated network that maintains consistent feature distributions without relying on batch statistics. Similarly, SimCLR applies BN only to the projection head, not the backbone network, to prevent leakage while still benefiting from normalization.
Developers should also consider alternatives to BN in SSL architectures. For example, layer normalization or group normalization can replace BN in scenarios where batch size is small or batch-level dependencies must be avoided. In vision transformers (ViTs) adapted for SSL, normalization layers are often applied after attention blocks to stabilize training without relying on batch-specific statistics. The choice depends on the SSL method: contrastive approaches might limit BN to specific components, while reconstruction-based methods (e.g., masked autoencoders) can use BN more freely since their tasks don’t involve cross-sample comparisons. Testing these choices empirically—such as ablating BN in different network sections—is crucial to balancing stability and avoiding shortcuts in SSL models.