jigsaw puzzle solving

**Jigsaw Puzzle Solving** is a **self-supervised pretext task where the model is trained to predict the correct spatial arrangement of shuffled image patches** — requiring the network to learn spatial relationships, object structure, and visual semantics. **How Does Jigsaw Puzzle Solving Work?** - **Process**: Divide image into a 3×3 grid (9 patches). Shuffle them into one of N predefined permutations. The network predicts which permutation was used. - **Permutations**: Typically 100-1000 selected permutations (out of 9! = 362,880 total). - **Architecture**: Siamese-style — each patch encoded independently, then combined for classification. - **Paper**: Noroozi & Favaro (2016). **Why It Matters** - **Spatial Reasoning**: Forces the model to understand spatial relationships between object parts. - **Feature Quality**: Learned features transfer well to object detection and segmentation tasks. - **Historical**: One of the pioneering pretext tasks that launched the self-supervised learning era. **Jigsaw Puzzle Solving** is **teaching AI spatial common sense** — learning that the head goes above the body and the legs go below, all without human labels.