neural architecture transfer

**Neural Architecture Transfer** is a **NAS technique that transfers architecture knowledge across different tasks or datasets** — reusing architectures or search strategies discovered on one task to accelerate the architecture search on a related task. **How Does Architecture Transfer Work?** - **Searched Architecture Reuse**: Use an architecture found on ImageNet as the starting point for a medical imaging task. - **Search Space Transfer**: Transfer the search space design (which operations to include) from one domain to another. - **Predictor Transfer**: Train a performance predictor on one task and fine-tune it for another. - **Meta-Learning**: Learn to search quickly from experience across many tasks. **Why It Matters** - **Cost Reduction**: Full NAS is expensive. Transferring reduces search time by 10-100x on new tasks. - **Cross-Domain**: Architectures discovered on natural images often transfer well to medical, satellite, or industrial vision. - **Practical**: Most practitioners don't have compute for full NAS — transfer makes it accessible. **Neural Architecture Transfer** is **leveraging architecture discoveries across tasks** — the observation that good architectural patterns generalize beyond the task they were found on.