# Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents

arXiv: https://arxiv.org/abs/1906.08102 · HTML: https://mlsanity.com/paper/1906.08102/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [CONetV2: Efficient Auto-Channel Size Optimization for CNNs](https://mlsanity.com/paper/2110.06830/) (2021)

## Abstract (arXiv)

Recent advances in Neural Architecture Search (NAS) have produced state-of-the-art architectures on several tasks. NAS shifts the efforts of human experts from developing novel architectures directly to designing architecture search spaces and methods to explore them efficiently. The search space definition captures prior knowledge about the properties of the architectures and it is crucial for the complexity and the performance of the search algorithm. However, different search space definitions require restarting the learning process from scratch. We propose a novel agent based on the Transformer that supports joint training and efficient transfer of prior knowledge between multiple search spaces and tasks.