Papers › Progressive Neural Architecture Search
Progressive Neural Architecture Search
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms. Our approach uses a sequential model-based optimization (SMBO) strategy, in which we search for structures in order of increasing complexity, while simultaneously learning a surrogate model to guide the search through structure space. Direct comparison under the same search space shows that our method is up to 5 times more efficient than the RL method of Zoph et al. (2018) in terms of number of models evaluated, and 8 times faster in terms of total compute. The structures we discover in this way achieve state of the art classification accuracies on CIFAR-10 and ImageNet.
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Code
Syntology Ran 3 of 3 code samples harvested from 2 repositories linked to this paper; 0 have no recorded run. Of those that ran: 3 ran · honoured contract.
By repository: official repository: 1 sample from 1 repository, 1 ran; community (archive-listed): 2 samples from 1 repository, 2 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
18 repositories listed; official and paper-mentioned ones first.
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
3 samples harvested; 3 ran; 3 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 2 of the 3 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | PNASNet-5 | GFLOPs | 50 | #490 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | PNASNet-5 | Number of params | 86.1M | #490 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | PNASNet-5 | Operations per network pass | 2.5G | #490 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | PNASNet-5 | Top 1 Accuracy | 82.9% | #490 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | PNASNet-5 | Top 5 Accuracy | 96.2 | #490 of 1060 | Archive leaderboard | report |
| Neural Architecture Search | ImageNet | PNAS | Params | 5.1 | #135 of 135 | Archive leaderboard | report |
| Neural Architecture Search | NAS-Bench-201, ImageNet-16-120 | PNAS + | Accuracy (Val) | 44.75 | #47 of 49 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: PNAS
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