swav

**SwAV** (Swapping Assignments between Views) is a **self-supervised learning method that combines contrastive learning with online clustering** — assigning augmented views to prototype vectors (cluster centers) and training the network to predict the assignment of one view from the representation of another. **How Does SwAV Work?** - **Prototypes**: Learnable cluster center vectors ${c_1, ..., c_K}$. - **Process**: Encode two views -> compute soft assignments (codes) to prototypes via Sinkhorn-Knopp -> train each view to predict the other view's assignment. - **Swapping**: The "swap" predicts view B's cluster assignment from view A's features, and vice versa. - **Multi-Crop**: Uses multiple small crops in addition to two standard crops for efficiency. **Why It Matters** - **Scalable**: No need for large negative sample pools (prototypes are compact representations of the dataset). - **Multi-Crop**: The multi-crop strategy provides a significant accuracy boost at minimal compute cost. - **Performance**: Competitive with BYOL and SimCLR on ImageNet benchmarks. **SwAV** is **learning by cluster matching** — using the structure of the dataset's natural clusters to guide representation learning.