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SotA/Methodology/AutoML/NATS-Bench Topology, ImageNet16-120

AutoML on NATS-Bench Topology, ImageNet16-120

Metric: Test Accuracy (higher is better)

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#Model↕Test Accuracy▼AugmentationsPaperDate↕Code
1EigenNas (Zhu et al., 2022)45.54NoGeneralization Properties of NAS under Activatio...2022-09-15-
2PPO (Schulman et al., 2017)44.95NoProximal Policy Optimization Algorithms2017-07-20Code
3NASI (Shu et al., 2021)44.84NoNASI: Label- and Data-agnostic Neural Architectu...2021-09-02-
4RE (Real et al., 2019)44.76NoRegularized Evolution for Image Classifier Archi...2018-02-05Code
5TE-NAS (Chen et al., 2021)42.38NoNeural Architecture Search on ImageNet in Four G...2021-02-23Code
6FairNAS (Chu et al., 2021)42.19NoFairNAS: Rethinking Evaluation Fairness of Weigh...2019-07-03Code
7KNAS (Xu et al., 2021)34.11NoKNAS: Green Neural Architecture Search2021-11-26Code