Online Self-supervised Learning

Standard SSL collects a large fixed dataset first, then trains for many epochs over it. Online SSL processes data sequentially as it arrives and updates the model incrementally, without revisiting the entire history — making it suitable for streaming or privacy-constrained settings where the full dataset is never available at once.

Not always. Some online SSL methods (such as those using distillation or slow-moving target networks) can mitigate forgetting without storing past examples. However, a small replay buffer generally improves representation quality; the trade-off is storage cost versus forgetting resistance.

Sparse data streams make learning difficult because pretext tasks such as contrastive learning need sufficient within-batch diversity to form useful negative pairs. Very small mini-batches or very slow arrival rates degrade representation quality; consider bootstrapping the model with offline pre-training first if possible.

Use periodic linear-probe evaluations on a fixed held-out labeled set — freeze the encoder at regular intervals and train a lightweight linear classifier on top. Tracking linear-probe accuracy over time reveals both learning progress and any catastrophic forgetting of earlier distributions.

Momentum-based self-distillation objectives (BYOL-style) and clustering-based targets (OBoW-style) tend to be more stable in the online setting than standard contrastive learning, because they are less sensitive to mini-batch size. The best choice depends on data modality and hardware budget.