NAS-Bench-101 is the first public architecture dataset for NAS research. To build NASBench-101, the authors carefully constructed a compact, yet expressive, search space, exploiting graph isomorphisms to identify 423k unique convolutional
architectures. The authors trained and evaluated all of these architectures multiple times on CIFAR-10 and compiled the results into a large dataset of over 5 million trained models. This allows researchers to evaluate the quality of a diverse range of models in milliseconds by querying the precomputed dataset.
Source: NAS-Bench-101: Towards Reproducible Neural Architecture Search
Variants: NAS-Bench-101
This dataset is used in 1 benchmark:
Task | Model | Paper | Date |
---|---|---|---|
Neural Architecture Search | DiNAS | Multi-conditioned Graph Diffusion for Neural … | 2024-03-09 |
Neural Architecture Search | LayerNAS | LayerNAS: Neural Architecture Search in … | 2023-04-23 |
Neural Architecture Search | GenNAS | Generic Neural Architecture Search via … | 2021-08-04 |
Recent papers with results on this dataset: