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Neural Architecture Search On Nats Bench

Métriques

Test Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Test Accuracy
Paper TitleRepository
EigenNas (Zhu et al., 2022)45.54Generalization Properties of NAS under Activation and Skip Connection Search-
LayerNAS46.58±0.59LayerNAS: Neural Architecture Search in Polynomial Complexity-
GreenMachine-2-GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NAS
FairNAS (Chu et al., 2021)42.19FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search
GreenMachine-3-GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NAS
TE-NAS (Chen et al., 2021)42.38Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective
NASI (Shu et al., 2021)44.84NASI: Label- and Data-agnostic Neural Architecture Search at Initialization-
GreenMachine-1-GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NAS
KNAS (Xu et al., 2021)34.11KNAS: Green Neural Architecture Search
RE (Real et al., 2019)44.76Regularized Evolution for Image Classifier Architecture Search
PPO (Schulman et al., 2017)44.95Proximal Policy Optimization Algorithms
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Neural Architecture Search On Nats Bench | SOTA | HyperAI