NAS-Bench-101

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Overview

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

Associated Benchmarks

This dataset is used in 1 benchmark:

Recent Benchmark Submissions

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

Research Papers

Recent papers with results on this dataset: