context prediction

**Context Prediction** is a **self-supervised pretext task where the model predicts the spatial relationship between two image patches** — given a center patch and a neighboring patch, the network classifies which of 8 relative positions (top-left, top, top-right, etc.) the neighbor occupies. **How Does Context Prediction Work?** - **Process**: Extract a center patch and one of its 8 surrounding patches. Create a gap between them to prevent trivial solutions (texture continuation). - **Classification**: 8-class problem (which direction?). - **Architecture**: Two-stream (each patch encoded independently) + concatenation + classifier. - **Paper**: Doersch et al. (2015) — one of the earliest SSL pretext tasks. **Why It Matters** - **Spatial Understanding**: Learns spatial relationships and object part co-occurrence patterns. - **Pioneering**: Among the first works demonstrating that self-supervised pretext tasks can learn transferable visual representations. - **Evolution**: Led to jigsaw puzzles, relative patch location, and eventually modern contrastive methods. **Context Prediction** is **the original spatial reasoning pretext task** — the granddaddy of self-supervised visual learning.