Methods › Computer Vision › Convolutional Neural Networks › Single-path NAS

Single-path NAS

5 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Single-Path NAS is a convolutional neural network architecture discovered through the Single-Path neural architecture search approach. The NAS utilises a single-path search space. Specifically, compared to previous differentiable NAS methods, Single-Path NAS uses one single-path over-parameterized ConvNet to encode all architectural decisions with shared convolutional kernel parameters. The approach is built upon the observation that different candidate convolutional operations in NAS can be viewed as subsets of a single superkernel. Without having to choose among different paths/operations as in multi-path methods, we instead solve the NAS problem as finding which subset of kernel weights to use in each ConvNet layer. By sharing the convolutional kernel weights, we encode all candidate NAS operations into a single superkernel.

The architecture itself uses the inverted residual block from MobileNetV2 as its basic building block.

Source: Single-Path NAS: Designing Hardware-Efficient ConvNets...See Code · skmhrk1209/Single-Path-NAS-PyTorch

Papers archive 2025-07-28

5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Image Classification5
Neural Architecture Search5
image-classification4
GPU3
General Classification2
AutoML1
Hyperparameter Optimization1

Usage over time archive 2025-07-28

Papers per year tagged with Single-path NAS: 2019 to 2023, peak 3 3 0 2019: 3 papers 2019 2020: 1 paper 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (5 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Convolutional Neural Networks

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