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홈뉴스최신 연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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Neural Architecture Search On Nats Bench 1

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Test Accuracy

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이 벤치마크에서 각 모델의 성능 결과

모델 이름
Test Accuracy
Paper TitleRepository
KNAS (Xu et al., 2021)93.05KNAS: Green Neural Architecture Search
GreenMachine-2-GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NAS
NASI (Shu et al., 2021)93.55NASI: Label- and Data-agnostic Neural Architecture Search at Initialization-
FairNAS (Chu et al., 2021)93.23FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search
GreenMachine-3-GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NAS
PPO (Schulman et al., 2017)94.02Proximal Policy Optimization Algorithms
LayerNAS94.34±0.12LayerNAS: Neural Architecture Search in Polynomial Complexity-
EigenNas (Zhu et al., 2022)93.46Generalization Properties of NAS under Activation and Skip Connection Search-
RE (Real et al., 2019)94.13Regularized Evolution for Image Classifier Architecture Search
TE-NAS (Chen et al., 2021)93.90Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective
GreenMachine-1-GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NAS
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